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Journal of Information Science
Journal Prestige (SJR): 0.674
Citation Impact (citeScore): 2
Number of Followers: 1082  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0165-5515 - ISSN (Online) 1741-6485
Published by Sage Publications Homepage  [1175 journals]
  • Predicting social media rumours in the context of public health
           emergencies

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      Authors: Ran Sun, Lu An, Gang Li, Chuanming Yu
      Abstract: Journal of Information Science, Ahead of Print.
      The spread of rumours on social media in the context of public health emergencies often distorts perceptions of public events and obstructs crisis management. Microblog entries about 28 rumour cases are collected on Sina Weibo during the COVID-19 outbreak. The Modality–Agency–Interactivity–Navigability model is used to identify the key factors of rumour prediction. To investigate the relationship among information modality, information content, information source and rumour identification, the binary logistic regression model is established based on the features of users and microblog entries. In addition, we propose a multi-feature rumour prediction model based on the Bidirectional Encoder Representations from Transformers (BERT) and Extreme Gradient Boosting (XGBoost) models. The proposed rumour prediction model has the best performance compared with other models. The feature importance is then calculated by the SHapley Additive exPlanations (SHAP), which can also explain the XGBoost results. It is shown that the likelihood that microblog entries are rumours decreases as the values of variables such as user influence and the positive sentiment of comments rise. Microblog entries posted on Thursdays or at noon are more probably to be rumours than those posted at other time. The proposed model can assist emergency management departments in establishing a feasible rumour prediction mechanism to guide public opinion against rumours.
      Citation: Journal of Information Science
      PubDate: 2022-11-26T10:29:36Z
      DOI: 10.1177/01655515221137879
       
  • QADM: A quality-aware data model for freelancing applications based on
           recommendation systems

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      Authors: Arwa Bokhari, Asma Cherif
      Abstract: Journal of Information Science, Ahead of Print.
      Freelancing systems such as Freelancer and Upwork have recently been increasing in popularity due to their ability to provide efficient solutions. Nevertheless, the success of freelancing depends on the delivery of high-quality freelanced output. Indeed, quality control is a major concern and is affected by the uncertainty stemming from human factors relating to the diversity of workers’ skills and the workers’ changing behaviour over time. This article proposes a quality-aware freelancing data model (QADM) that takes into account both the diversity of worker skills and workers’ changing behaviour. The QADM is comprised of three submodels: skill, task and worker. The main goal is to model worker quality appropriately, and it does so by effectively modelling (1) worker suitability for new tasks, (2) worker reputation, (3) worker accuracy in completed tasks and (4) worker expected accuracy in new tasks. To improve the worker accuracy estimation, a task-to-task similarity algorithm is developed that achieves higher accuracy than Cos(topic), Cos(tf-idf) and Jaccard similarity methods. The quality-aware task assignment decision problem is solved as a top-k task recommendation problem. The results achieved in this article show that the QADM accomplishes a high recommender system mean average precision for the assignment decisions.
      Citation: Journal of Information Science
      PubDate: 2022-11-24T07:04:37Z
      DOI: 10.1177/01655515221136232
       
  • Hidden in the light: Scientists’ online presence on institutional
           websites and professional networking sites

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      Authors: Laura Paruschke, Axel Philipps
      Abstract: Journal of Information Science, Ahead of Print.
      The visibility of individual scientists and their academic performance plays a major role in gaining the creditability to get funded and to advance in academic positions. Therefore, web presences are increasingly used to boost one’s own visibility, disseminate research results and keep up to date with the research of others. However, previous reports show that these channels are not used equally by all scientists. Our study therefore investigates how faculty members (N = 868) at all universities in Lower Saxony (Germany) in the disciplines of physics, biology and chemistry present themselves on institutional websites and professional networking sites. We find that online presentations on institutional websites are mostly rudimentary. In contrast, there are more informative self-presentations on professional networking sites for both established (professors) and less-established (only PhD holders) faculty members. Our figures confirm observations that scientists present themselves online, but less-established ones seem to find less-supportive environments in academic institutions.
      Citation: Journal of Information Science
      PubDate: 2022-11-23T11:26:09Z
      DOI: 10.1177/01655515221137878
       
  • Young informal carers’ information needs communicated online:
           Professional and personal growth, finance, health and relationships

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      Authors: Kushwanth Koya, Gobinda Chowdhury, Emma Green
      Abstract: Journal of Information Science, Ahead of Print.
      Young informal carers (YICs) are non-professional young individuals providing care and support in various forms, usually to immediate family members, afflicted from a diverse range of both long- and short-term health conditions. Although there is significant knowledge about the information needs of adult carers in general, information needs and information seeking characteristics of the YICs’ community are understudied and are different. This study aims to identify the information needs of YICs communicated over the Internet and understanding their information seeking characteristics through a three-stage qualitative content analysis of posts written by YICs on two notable Internet forums. The analysis of 323 posts dated between March 2010 and April 2019 finds YICs’ needs are categorised by two types of online expression of needs, situational and information. Situational needs are illustrations of current difficult conditions and information needs are direct requests for information. Under situational and information needs, we identify four types of needs expressed: personal and professional growth, health (self and caree), finance and relationships. In addition, the findings indicate 94.36% posts in the sample as situational needs, which depict the uncertainty experienced by YICs under caring circumstances. The findings can assist government organisations and charities by improving the indexing of advice pages of their websites appropriate to the YICs’ search words, better availability of information and advertising, in addition to building quality mobile applications or digital support tools.
      Citation: Journal of Information Science
      PubDate: 2022-11-23T11:19:42Z
      DOI: 10.1177/01655515221136829
       
  • Improving the recommendation accuracy of TrustSVD via trustworthy analysis
           in the social network environment

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      Authors: Ruoxi Sun, Jun Yan, Fenghui Ren
      Abstract: Journal of Information Science, Ahead of Print.
      Recommender systems help Internet users quickly find information they may be interested in from an extremely large amount of resources. Recent studies have shown that incorporating auxiliary social trust relationship information into the recommender system improves the accuracy of recommendations. Most existing research only considers explicit trust relationships, which result in sub-optimal recommendation performance. In this research, we present a trust model which analyses user trustworthiness based on user’s behaviours on the social networks. The proposed trust model increases the density of trust relationships by considering explicit and implicit social trust relationships and also reflects a more fine-grained and realistic trust level between users. This improved social trust information is then incorporated into TrustSVD, a matrix factorisation–based social recommendation method. By analysing the prediction result using a real-world data set, Douban-600k from the Douban Movie website, we found that our proposed method provides more accurate predictions compared with SVD++ and traditional TrustSVD, improving users’ experiences.
      Citation: Journal of Information Science
      PubDate: 2022-11-23T11:09:28Z
      DOI: 10.1177/01655515221136221
       
  • An improved author-topic (AT) model with authorship credit allocation
           schemes

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      Authors: Shuo Xu, Ling Li, Congcong Wang, Xin An, Guancan Yang
      Abstract: Journal of Information Science, Ahead of Print.

      Authors hip credit allocation schemes have attracted considerable research attention. However, no consensus about which one is the best has been attained until now, and limited evidence from practical tasks has been reported. Therefore, this study uses the author interest discovery task as a real-world task case to provide valuable insights into authorship credit allocation schemes and guidelines for further practical applications. For this purpose, a novel model, ATcredit, is proposed to strengthen the Author-Topic (AT) model with an authorship credit allocation scheme, and collapsed Gibbs sampling is used to approximate the posterior and estimate model parameters. Extensive experiments using the SynBio dataset reveal several interesting findings as follows. (a) Any scheme for allocating unequal authorship credits performs better than its equal-credit counterpart with our ATcredit model in terms of perplexity. (b) The fixed versions of four out of the six schemes work better than their flexible counterparts with our ATcredit model, regardless of the hyper-authorship strategy. (c) The variation coefficient of credit awards can serve as a criterion to decide whether the hyper-authorship strategy should be used. (d) When the number of authors in a scholarly article is less than three, the six authorship credit allocation schemes are similar to each other with our ATcredit model in terms of perplexity. (e) The harmonic counting scheme performs the best, followed by the arithmetic counting scheme, and the network-based counting scheme performs the worst with our ATcredit model in terms of perplexity. (f) The arithmetic counting scheme is similar to the harmonic counting scheme in terms of the normalised mutual information (NMI) of discovered interests, but the geometric counting scheme is different from the axiomatic and network-based counting schemes.
      Citation: Journal of Information Science
      PubDate: 2022-11-23T10:17:09Z
      DOI: 10.1177/01655515221133530
       
  • UFTDRDH: A novel user-oriented solution for full-text database retrieval
           in Chinese digital humanities

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      Authors: Chengxi Yan, Xiaoke Fang, Yang Li
      Abstract: Journal of Information Science, Ahead of Print.
      Information technology boosts the development of database retrieval in the Chinese digital humanities domain. However, most database providers adopt a system-oriented design pattern, which fails to handle the problem of query gaps in users’ retrieval process. This issue seriously hinders the effective use of database retrieval functionalities, peculiarly among those historical and humanities researchers. To address it, we propose UFTDRDH, a novel user-oriented solution based on automatic query formulation (AQF) technologies, which integrates a human–machine interactive module for the selection of new query-related expansion terms and a powerful query expansion algorithmic component (UFTDRDH-QEV) optimised by a topic-enhancing relevance feedback model approach (ToQE). To verify the effectiveness of UFTDRDH, several comparative experiments are conducted, including quantitative evaluation for retrieval efficiency and user satisfaction, as well as qualitative studies for interpretative traceability. The empirical results are multidimensional and robust, which not only shows the positive effects of different AQFs on gap reduction, especially the importance of query expansion as the most effective technology, but also underlines the remarkably advantageous performance of UFTDRDH compared with traditional system-oriented automatic query expansion in different task contexts. We believe the application of UFTDRDH can further strengthen the research focus on user-centred design and improve the level of current full-text database retrieval in the field of Chinese digital humanities. Broadly speaking, this solution can be also extended to the full-text database retrieval in other languages and digital humanities domains.
      Citation: Journal of Information Science
      PubDate: 2022-11-22T01:45:25Z
      DOI: 10.1177/01655515221133529
       
  • Exploiting tweet sentiments in altmetrics large-scale data

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      Authors: Saeed-Ul Hassan, Naif Radi Aljohani, Usman Iqbal Tarar, Iqra Safder, Raheem Sarwar, Salem Alelyani, Raheel Nawaz
      Abstract: Journal of Information Science, Ahead of Print.
      This article aims to exploit social exchanges on scientific literature, specifically tweets, to analyse social media users’ sentiments towards publications within a research field. First, we employ the SentiStrength tool, extended with newly created lexicon terms, to classify the sentiments of 6,482,260 tweets associated with 1,083,535 publications provided by Altmetric.com. Then, we propose harmonic means-based statistical measures to generate a specialised lexicon, using positive and negative sentiment scores and frequency metrics. Next, we adopt a novel article-level summarisation approach to domain-level sentiment analysis to gauge the opinion of social media users on Twitter about the scientific literature. Last, we propose and employ an aspect-based analytical approach to mine users’ expressions relating to various aspects of the article, such as tweets on its title, abstract, methodology, conclusion or results section. We show that research communities exhibit dissimilar sentiments towards their respective fields. The analysis of the field-wise distribution of article aspects shows that in Medicine, Economics, Business and Decision Sciences, tweet aspects are focused on the results section. In contrast, in Physics and Astronomy, Materials Sciences and Computer Science, these aspects are focused on the methodology section. Overall, the study helps us to understand the sentiments of online social exchanges of the scientific community on scientific literature. Specifically, such a fine-grained analysis may help research communities in improving their social media exchanges about the scientific articles to disseminate their scientific findings effectively and to further increase their societal impact.
      Citation: Journal of Information Science
      PubDate: 2022-11-17T09:21:43Z
      DOI: 10.1177/01655515211043713
       
  • Extending ontology pitfalls for better ontology evaluation

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      Authors: Tuğba Özacar
      Abstract: Journal of Information Science, Ahead of Print.
      This article presents a framework that detects potential ontology building errors to improve the ontology quality. These potential errors are called ontology pitfalls in the literature. This work extends the existing ontology pitfall set in the literature and suggests new solutions for ontology repair. We have also developed a Java implementation for detection of the proposed pitfalls. These pitfalls were evaluated with well-known ontologies using this implementation.
      Citation: Journal of Information Science
      PubDate: 2022-11-11T08:54:12Z
      DOI: 10.1177/01655515221110990
       
  • Big data curation framework: Curation actions and challenges

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      Authors: Ayoung Yoon, Jihyun Kim, Devan Ray Donaldson
      Abstract: Journal of Information Science, Ahead of Print.
      Big data curation represents an emerging topic of inquiry but still in an early phase along its adoption curve. The term big data itself is a nebulous concept, and the differences between small data curation and big data curation are nuanced. The goal of this research is to provide a theoretical framework that identifies big data curation actions and associated curation challenges. This study is based on the practices of big data research and data curation by systematically examining literature. The outcome of the study includes the big data curation framework that provides overview of curation activities and concerns that are essential to perform such activities. The study also provides practical implications for libraries, archives, data repositories and other information organisations that concerns the issue of big data curation as big data presents a multidimensional array of exigencies in relation to the mission of those organisations.
      Citation: Journal of Information Science
      PubDate: 2022-11-11T08:50:32Z
      DOI: 10.1177/01655515221133528
       
  • Experimenting the effect of using visual multimedia intervention to
           inculcate social media literacy skills to tackle fake news

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      Authors: Leilei Zhang, Timothy Onosahwo Iyendo, Oberiri Destiny Apuke, Celestine Verlumun Gever
      Abstract: Journal of Information Science, Ahead of Print.
      This study experimented the effect of using visual multimedia intervention to improve users’ social media literacy skills to combat fake news. We carried out a quasi-experiment in one public university in Nigeria and randomly divided 470 participants into equal parts to form a control and experimental group. The respondents in the experimental group were exposed to 8 weeks of training using visual multimedia to improve their social media literacy skills to fight fake news. We realised that those exposed to social media literacy skills training via visual multimedia demonstrated a better knowledge of social media, better recognition of fake news, a higher tendency to verify information and a lesser inclination to share false news. Implications for research and practice were discussed.
      Citation: Journal of Information Science
      PubDate: 2022-11-01T09:58:09Z
      DOI: 10.1177/01655515221131797
       
  • Content validity and reliability of an instrument to investigate
           serendipitous retrieval of information: Information Encountering and its
           Subsequent Keeping and Sharing Behaviour Scale (IEKSBS)

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      Authors: Waqar Ahmad Awan, Kanwal Ameen, Saira Soroya
      Abstract: Journal of Information Science, Ahead of Print.
      Good quantitative survey research is based on valid and reliable instruments. However, when it comes to information encountering (IE) and its keeping (IK) and sharing (IS) behaviours, there was currently no instrument available to accurately measure it. Therefore, this study was designed to prepare a valid and reliable instrument to investigate IE and its subsequent behaviours. This comprised of its preferred keeping tools, communication channels, purposes of sharing, motivators to share and constraints in sharing. Three already available instruments in the information behaviours lacked the distinguished type of information. These instruments were merged after seeking permission from their original authors. The merged instrument was validated for content and reliability. The data for reliability checking were collected twice: first, at the time of the pilot study and second, at the time of the final study. A multi-stage purposive total population sampling technique was used to collect the data from the sample. The subsequent instrument proved to be reliable for the investigation of IE, IK and IS behaviours. The process of making an instrument valid and reliable is complex. Therefore, the steps taken to establish content validity and reliability of the instrument are presented in detail in this article. The instrument is attached in the end for future studies and research.
      Citation: Journal of Information Science
      PubDate: 2022-10-31T12:50:50Z
      DOI: 10.1177/01655515221128756
       
  • Sentiment analysis using lexico-semantic features

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      Authors: Mudasir Mohd, Saheeba Javeed, Nowsheena, Mohsin Altaf Wani, Hilal Ahmad Khanday
      Abstract: Journal of Information Science, Ahead of Print.
      Sentiment analysis of the text deals with the mining of the opinions of people from their written communication. With the increasing usage of online social media platforms for user interactions, abundant opinionated textual data emerges. Therefore, it leads to increased mining of opinions and sentiments and hence greater interest in sentiment analysis. The article introduces the novel Lexico-Semantic features and their use in the sentiment polarity task of English language text. These features are derived using the semantic extension of the lexicons by employing sentiment lexicons and semantic models. These features make data sample size consistent when used in deep learning settings, thereby eliminating the zero padding. For evaluation, we use different semantic models and lexicons to determine the role and impact of Lexico-Semantic features in classification performance. These features, along with the other features, are used to train the different classifiers. Our experimental evaluation shows that introducing Lexico-Semantic features to various state-of-the-art methods of both machine and deep learning improves the overall performance of classifiers.
      Citation: Journal of Information Science
      PubDate: 2022-10-31T12:41:10Z
      DOI: 10.1177/01655515221124016
       
  • Effect of perceived risks, perceived benefits and regulatory events on
           users’ supervision intention towards e-hailing platforms: A mixed method
           

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      Authors: Guoyin Jiang, Wanqiang Yang, Na Zhang
      Abstract: Journal of Information Science, Ahead of Print.
      Research on platform governance has gained extensive scholarly interest. However, studies on user participation intention towards platform supervision remain underexplored. In this study, a mixed method approach, including qualitative and quantitative methods, is used to explore the factors influencing the supervision intention of e-hailing users. A preliminary interview study is conducted to determine what factors impact users’ supervision intention, and an SEM model is tested using 359 survey data to examine how perceived government regulation, perceived risks/benefits and regulatory events impact supervision intention. Results indicate that perceived government regulation reduces perceived risks and increases the perceived benefits from participation into supervision, perceived risks weakened users’ supervision intention, whereas perceived benefits stimulated it. Perceived risks and perceived benefits positively mediated the relationship between perceived government regulation and users’ supervision intention. Regulatory events have a negative moderating effect on the relationship between e-hailing users’ perceived risks (benefits) and supervision intention.
      Citation: Journal of Information Science
      PubDate: 2022-10-25T11:32:18Z
      DOI: 10.1177/01655515221128422
       
  • The phronesis of expert searchers on using forward citation searching and
           web searching to search for studies for systematic reviews: A hermeneutic
           phenomenological analysis

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      Authors: Simon Briscoe, Rebecca Abbott, G J Melendez-Torres
      Abstract: Journal of Information Science, Ahead of Print.
      Systematic reviews aim to use formalised and explicitly described methods. However, studies show that systematic reviews pose challenges which can only be resolved using expert judgement that is resistant to explicit formulation. The expertise required to make such judgements can be understood as practical knowledge or phronesis, and is based on lived experiences rather than following clearly defined rules. This study used qualitative methods to investigate the phronesis of expert searchers in the development and conduct of searches for studies for systematic reviews. In particular, the study focused on two ‘supplementary’ search methods: forward citation searching and web searching. Data collection used semi-structured interviews with 15 expert searchers and the analysis used a hermeneutic phenomenological approach. The findings describe five habits of phronesis when searching for studies: Outcome-oriented; persistent; adaptive; critically engaged and holistic. The study brings attention to the use of expert judgement when searching for studies for systematic reviews.
      Citation: Journal of Information Science
      PubDate: 2022-10-14T07:13:34Z
      DOI: 10.1177/01655515221130237
       
  • What determines online rumour sharing on COVID-19' A
           stimulus–response framework

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      Authors: Feng Guo, Apan Zhou, Peng Luo
      Abstract: Journal of Information Science, Ahead of Print.
      Based on the stimulus–response framework, this study examines the external environmental stimuli influencing online rumour sharing about COVID-19 and considers the contingent effect of fear. A large-scale online survey was used to test the proposed research model and hypotheses. The final data set comprised 2807 valid responses. The results indicate that perceptions of community safety and infection risk negatively affect online rumour sharing, while social influence positively affects online rumour sharing. Fear weakens the negative effects of community safety on online rumour sharing but strengthens the positive effect of social influence on online rumour sharing. This study provides a comprehensive analysis by applying the stimulus–response framework to explore the underlying drivers of online rumour sharing with regard to COVID-19 and the moderating effects of fear in the Chinese context.
      Citation: Journal of Information Science
      PubDate: 2022-10-14T07:09:44Z
      DOI: 10.1177/01655515221126989
       
  • Exploring the diversity and consistency of China’s information
           technology policy

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      Authors: Chao Yang, Cui Huang
      Abstract: Journal of Information Science, Ahead of Print.
      Information technology (IT) policies have played an indispensable role in China’s IT research and development (R&D) and industrial development. However, there has been a lack of quantitative and clear understanding of the consistency and diversity of China’s IT policy mixes. In this article, we constructed policy target network to simulate the real policy mixes. Based on the distribution and evolution of networks, we identify and analyse the ‘diversity’ and ‘consistency & continuity’ of China’s IT policy. Our results show that China’s IT policies cover 12 different demands, and each demand corresponds to 5–10 core policy goals. At the same time, we divide the history of IT policy into seven periods and compare the characteristics of each period. Despite the scale of the policy mixes continuing to expand, the synergy between different policy targets has become clearer, forming unique communities. Among them, there are 25 core policy targets that reflect varying degrees of consistency and continuity. This study not only deepens the understanding of China’s policy characteristics and patterns, but also provides a quantitative framework for the assessment of policy consistency and diversity.
      Citation: Journal of Information Science
      PubDate: 2022-10-13T12:48:40Z
      DOI: 10.1177/01655515221128417
       
  • Modelling the effects of personal factors on information security
           awareness

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      Authors: Ebru Albayrak, Hakkı Bağcı
      Abstract: Journal of Information Science, Ahead of Print.
      According to research, the number of attacks on the Internet has been increasing each year. Hence, information security awareness is a very significant skill. Accordingly, this study aimed to investigate the effects of students’ personal factors on their information security awareness. The researchers conducted a quantitative study that examines a theoretical model. The data were collected from 684 undergraduate students via three data tools. The effects of variables on information security awareness were explained via path analysis. The mediating role of technology attitude was examined in the relationship between information security awareness and the individual variables for the first time. The findings showed that gender and grade did not directly affect information security awareness levels, while attending information security training, department and technology attitude had a significant effect. On the other hand, some personal factors indirectly affected information security awareness. This analysis offered substantial contributions to the literature in uncovering the effects of variables on students’ information security awareness in a holistic way. The results can guide planning for information security training to increase information security awareness by considering personal factors.
      Citation: Journal of Information Science
      PubDate: 2022-10-13T12:43:29Z
      DOI: 10.1177/01655515221127609
       
  • Research on the co-evolution of temporal networks structure and public
           opinion propagation

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      Authors: Jiakun Wang, Hao Yu, Yun Li
      Abstract: Journal of Information Science, Ahead of Print.
      Under the new media environment, social platforms, as the carrier of information propagation, have shown a drastic change in their form and structure, endowing public opinion with unique propagation characteristics. In view of this, considering the dynamic changes of online social network (OSN) structure, this article intends to analyse the spreading mechanism of public opinion in temporal networks and improve the applicability of public opinion governance strategies. Combing the changes of OSN topology with the classical susceptible–infected–recovered (SIR) dynamics model, we constructed a co-evolution model of temporal networks structure and public opinion propagation, and the propagation threshold of public opinion was derived with the help of Markov process. Then, the propagation characteristics of public opinion in temporal networks and their co-evolution process under different factors were discussed through simulation experiments. The results show that the propagation of public opinion in temporal networks has faster speed and wider scope compared with that in static networks; netizens’ social activity has a phased impact on the evolution process of public opinion and with its significant heterogeneity, the propagation of public opinion is gradually suppressed; compared with [math], the evolution process of public opinion in temporal networks is more sensitive to the state change of public opinion [math]. Our research can further enrich the theoretical research system of network science and information science and also provide a certain decision-making reference for the regulators to reasonably govern the propagation of public opinion in social platforms.
      Citation: Journal of Information Science
      PubDate: 2022-10-13T12:38:40Z
      DOI: 10.1177/01655515221121944
       
  • Is this question going to be closed' Answering question closibility on
           Stack Exchange

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      Authors: Pradeep Kumar Roy, Jyoti Prakash Singh, Snehasish Banerjee
      Abstract: Journal of Information Science, Ahead of Print.
      Community question answering sites (CQAs) are often flooded with questions that are never answered. To cope with the problem, experienced users of Stack Exchange are now allowed to mark newly posted questions as closed if they are of poor quality. Once closed, a question is no longer eligible to receive answers. However, identifying and closing subpar questions takes time. Therefore, the purpose of this article is to develop a supervised machine learning system that predicts question closibility, the possibility of a newly posted question to be eventually closed. Building on extant research on CQA question quality, the supervised machine learning system uses 17 features that were grouped into four categories, namely, asker features, community features, question content features and textual features. The performance of the developed system was tested on questions posted on Stack Exchange from 11 randomly chosen topics. The classification performance was generally promising and outperformed the baseline. Most of the measures of precision, recall, F1-score and area under the receiver operating characteristic curve (AUC) were above 0.90 irrespective of the topic of questions. By conceptualising question closibility, the article extends previous CQA research on question quality. Unlike previous studies, which were mostly limited to programming-related questions from Stack Overflow, this one empirically tests question closibility on questions from 11 randomly selected topics. The set of features used for classification offers a framework of question closibility that is not only more comprehensive but also more parsimonious compared with prior works.
      Citation: Journal of Information Science
      PubDate: 2022-10-13T12:35:39Z
      DOI: 10.1177/01655515221118665
       
  • Knowledge-graph-based explainable AI: A systematic review

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      Authors: Enayat Rajabi, Kobra Etminani
      Abstract: Journal of Information Science, Ahead of Print.
      In recent years, knowledge graphs (KGs) have been widely applied in various domains for different purposes. The semantic model of KGs can represent knowledge through a hierarchical structure based on classes of entities, their properties, and their relationships. The construction of large KGs can enable the integration of heterogeneous information sources and help Artificial Intelligence (AI) systems be more explainable and interpretable. This systematic review examines a selection of recent publications to understand how KGs are currently being used in eXplainable AI systems. To achieve this goal, we design a framework and divide the use of KGs into four categories: extracting features, extracting relationships, constructing KGs, and KG reasoning. We also identify where KGs are mostly used in eXplainable AI systems (pre-model, in-model, and post-model) according to the aforementioned categories. Based on our analysis, KGs have been mainly used in pre-model XAI for feature and relation extraction. They were also utilised for inference and reasoning in post-model XAI. We found several studies that leveraged KGs to explain the XAI models in the healthcare domain.
      Citation: Journal of Information Science
      PubDate: 2022-09-24T08:32:01Z
      DOI: 10.1177/01655515221112844
       
  • Listening to the unheard and unseen: Information literacy perspectives of
           the rural bi/multilinguals in Nigeria

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      Authors: Ebubechukwu A Okonkwo, Bulorgbamu Cocodia, Ebubechukwu E Uba
      Abstract: Journal of Information Science, Ahead of Print.
      This article aims to investigate information literacy perspectives of bi/multilinguals in Nigeria. The study aims to identify bilingual and multilingual definitions of information literacy, determine factors that influence their information literacy needs and at what point bi/multilinguals need it. This implies that individuals who can speak up to two or three native languages would be used as the population of the study. Despite being a multicultural country, some languages are more recognised in Nigeria and as such, those groups of people are recognised. This research aims to listen to the unheard and unseen individuals who are the rural indigenes while considering a major influencing factor in the study which is language. A focus group interview was used and a transcript-based analysis was used for the study. The results indicate that information literacy is being able to communicate with their environment but more so involves giving back to the society through languages. These findings can provide a solid ground on which inclusion of rural indigenes can be formed. It provides a platform on which library advocacy for the inclusion of the rural community indigenes to enable them to express their form of information literacy and produce intellectual works in indigenous languages can be formed.
      Citation: Journal of Information Science
      PubDate: 2022-09-24T07:28:09Z
      DOI: 10.1177/01655515221118050
       
  • To know or not to know' Exploring COVID-19 information seeking with
           the risk information seeking and processing model

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      Authors: Xianlin Jin, Derek Lane
      Abstract: Journal of Information Science, Ahead of Print.
      To cope with the COVID-19 pandemic and reduce uncertainty, the public needs accurate and timely information. Inspired by the risk information seeking and processing (RISP) model, this article discovers the significant predictors of individuals’ COVID-19 information-seeking intention and behaviour. Overall, 394 adult participants from 47 states completed this study’s online survey. The hierarchical regression analysis reveals that risk experience and informational subjective norms are the most substantial predictors of individuals’ online information-seeking behaviour about COVID-19. Information insufficiency did not predict information seeking, and participants tend to overestimate their knowledge about COVID-19. RISP variables tend to share power in explaining the variances of information-seeking behaviour. Moreover, both channel beliefs and perceived information gathering capacity moderate information insufficiency’s prediction of information-seeking intention. These findings will assist researchers in discovering the fundamental motivation of information seeking. This article can guide pragmatic interventions to reduce the public’s uncertainty and mitigate the risk.
      Citation: Journal of Information Science
      PubDate: 2022-09-22T07:05:26Z
      DOI: 10.1177/01655515221125325
       
  • The cross-subsidy and buy-one-give-one models of compensated peer review:
           A comparative study for mission-driven journals

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      Authors: Jose A García, Rosa María Rodriguez-Sánchez, J Fdez-Valdivia
      Abstract: Journal of Information Science, Ahead of Print.
      A financial compensation model could incentivise peer reviewers to provide the optimal amount of effort by rewarding them for their quality reports. Therefore, in this article, we consider peer-reviewed journals with the mission to financially compensate reviewers for delivering quality reports and to ensure affordable access to peer review for every author. In a cross-subsidy scenario of compensated peer review, the mission-driven journals expect all authors to pay for either the standard or a premium peer review. However, using this strategy, the journals offer standard peer review to low-income authors at lower prices, compared with the premium price high-income authors pay for higher-quality peer review. In addition, we also present a buy-one-give-one model of compensated peer review for the mission-driven journals. In this alternative setting, when a high-income author pays for a premium peer review, the journals would donate a certain number of free peer reviews to the low-income authors who are unable to afford them. In this two-tiered system, scholars with access to more funding receive premium treatment, while low-income authors can still access standard peer review similar to what authors currently receive for free. In this article, we show a comparative study between the cross-subsidy and buy-one-give-one models of compensated peer review. We find that a buy-one-give-one scenario provides a higher total sum of financial and social gain than the cross-subsidy scenario either when the mission-driven journals are highly socially responsible or when the social gap between the high-income and the low-income authors is large enough.
      Citation: Journal of Information Science
      PubDate: 2022-09-22T07:02:06Z
      DOI: 10.1177/01655515221125321
       
  • Studying the cognitive relatedness between topics in the global science
           landscape: The case of Big Data research

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      Authors: Xiaozan Lyu, Rodrigo Costas
      Abstract: Journal of Information Science, Ahead of Print.
      Taking Big Data research as a case study, this article intends to investigate the cognitive relatedness of research topics across the global science landscape to a focal topic. Several levels of cognitive relatedness are established depending on the citation distance between the citing publications and a core set of publications. The concept of citation generation is adopted for identifying and classifying other publications with different levels of relatedness to the core set. The micro publication-level classification system of Centre for Science and Technology Studies (CWTS) is applied for determining clusters of publication sets at the topic level. The overall cognitive relatedness of micro clusters to Big Data core publications are measured based on the mean citation generation of all the publications in corresponding clusters. In addition to the given clusters, this study also explores the ‘topics’ relatedness from a semantic point of view, by extracting high-frequency title terms of publications in each generation. Results show that data analysis methods and technologies are the topics with the strongest cognitive relatedness to Big Data research, while topics on physics and astronomy studies present the weakest relatedness. This approach allows assessment of relatedness between research topics by considering the citations distribution across multiple citation generations, and can provide useful insights to study and characterise topics with fuzzy boundaries or are difficult to delineate, thus representing a novel toolset relevant in the context of studying interdisciplinary research.
      Citation: Journal of Information Science
      PubDate: 2022-09-19T06:18:45Z
      DOI: 10.1177/01655515221121970
       
  • Agency and liminality during the COVID-19 pandemic: Why information
           literacy cannot fix vaccine hesitancy

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      Authors: Alison Hicks, Annemaree Lloyd
      Abstract: Journal of Information Science, Ahead of Print.
      This article employs a sociological and dialogical information perspective to identify what shape information literacy practice takes for people who are hesitant about the COVID-19 vaccine. An information perspective places information and people’s relations with information at the centre of the inquiry. The study carried out 14 semi-structured interviews with UK adults who had not yet received or taken up their invitation to have the COVID-19 vaccine. Outcomes of this study suggest that information literacy practices related to vaccine hesitancy emerged through the liminal space and in relation to agentic performance, which was catalysed through engagement with experiential, corporeal and social information. This study has implications for the teaching of information literacy, in particular, the idea that being informed is an affirmative action that will automatically empower learners to make appropriate choices.
      Citation: Journal of Information Science
      PubDate: 2022-09-16T10:47:53Z
      DOI: 10.1177/01655515221124003
       
  • Locality sensitive blocking (LSB): A robust blocking technique for data
           deduplication

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      Authors: Asif Sohail, Waqar ul Qounain
      Abstract: Journal of Information Science, Ahead of Print.
      Data deduplication is process of discovering multiple representations of same entity in an information system. Blocking has been a benchmark technique for avoiding the pair-wise record comparisons in data deduplication. Standard blocking (SB) aims at putting the potential duplicate records in the same block on the basis of a blocking key. Afterwards, the detailed comparisons are made only among the records residing in the same block. The selection of blocking key is a tedious process that involves exponential alternatives. The outcome of SB varies considerably with a change in blocking key. To this end, we have proposed a robust blocking technique called Locality Sensitive Blocking (LSB) that does not require the selection of blocking key. The experimental results show an increase of up to 0.448 in F-score as compared with SB. Furthermore, it is found that LSB is more robust towards blocking parameters and data noise.
      Citation: Journal of Information Science
      PubDate: 2022-09-16T10:43:33Z
      DOI: 10.1177/01655515221121963
       
  • Information overload and misinformation sharing behaviour of social media
           users: Testing the moderating role of cognitive ability

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      Authors: Oberiri Destiny Apuke, Bahiyah Omar, Elif Asude Tunca, Celestine Verlumun Gever
      Abstract: Journal of Information Science, Ahead of Print.
      Sharing of misinformation on social media platforms is a global concern, with research offering little insight into the motives behind such sharing. Drawing from the cognitive load theory and literature on cognitive ability, we developed and tested a research model hypothesising why people share misinformation. We also tested the moderating role of cognitive ability. We obtained data from 385 social media users in Nigeria using a chain referral technique with an online questionnaire as the instrument for data collection. Our findings suggest that information overload and social media fatigue are strong predictors of misinformation sharing. Information stress also contributed to misinformation sharing behaviour. Furthermore, cognitive ability moderated and weakened the effect information strain and information overload have on misinformation sharing in such a way that this effect is more pronounced among those with low cognitive ability. This indicates that those with low cognitive ability have a higher tendency to share misinformation. However, cognitive ability had no effect on the effect social media fatigue has on misinformation sharing behaviour. The study concluded with some theoretical and practical implications.
      Citation: Journal of Information Science
      PubDate: 2022-09-16T10:33:31Z
      DOI: 10.1177/01655515221121942
       
  • Sentiment analysis of Indian Tweets about Covid-19 vaccines

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      Authors: Aasif Ahmad Mir, Rathinam Sevukan
      Abstract: Journal of Information Science, Ahead of Print.
      People are becoming more reliant on social media networks to express their opinions about various topics and obtain health information. The study is intended to explore and analyse the sentiments of Indian people related to Covid-19 vaccines as well as to visualise the top most frequently occurring terms individuals have used to communicate their ideas on Twitter about Covid-19 vaccines in India. The Tweet Archiver was used to retrieve the Tweets against ‘Covid19vaccine’ and ‘Coronavirusvaccine’ hashtags for the period of 2 months 18 days (4 January 2021–22 March 2021). After collecting data, the Orange software and VOSviewer were used for further analysis. The Tweets were posted across the country, with an immense contribution from Maharashtra (223, 15.58%), followed by Delhi (220, 15.37%) and Tamil Nadu (73, 5.10%). The majority (639, 44.65%) of the Tweets reflect positive sentiments, followed by neutral (521, 38.50%) and negative (241, 16.84%) sentiments, respectively. This signifies that most Twitter users have a favourable opinion towards Covid vaccines in India. Based on the relevance score of the words, the words ‘Delhi heart’, ‘Lung institute’, ‘Gift’, ‘Unite2fightcorona’, and ‘Covid-19 Vaccine’ are the leading words appearing in Tweets. The study illustrates the sentiments of the Indian people towards ‘Covid-19 vaccines’, gains some insights into overall public communication about the topic and complements the existing literature. It can assist health policymakers and administrators in better understanding the polarity (positive, negative, and neutral) of Tweets about Covid-19 vaccines on Twitter to raise public awareness about health concerns and misinformation about the vaccine.
      Citation: Journal of Information Science
      PubDate: 2022-09-16T10:29:04Z
      DOI: 10.1177/01655515221118049
       
  • A Tailored Approach: A model for literature searching in complex
           systematic reviews

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      Authors: Chris Cooper, Andrew Booth, Kerryn Husk, Rebecca Lovell, Julia Frost, Ute Schauberger, Nicky Britten, Ruth Garside
      Abstract: Journal of Information Science, Ahead of Print.
      Our previous work identified that nine leading guidance documents for seven different types of systematic review advocated the same process of literature searching. We defined and illustrated this process and we named it ‘the Conventional Approach’. The Conventional Approach appears to meet the needs of researchers undertaking literature searches for systematic reviews of clinical interventions. In this article, we report a new and alternate process model of literature searching called ‘A Tailored Approach’. A Tailored Approach is indicated as a search process for complex reviews which do not focus on the evaluation of clinical interventions. The aims of this article are to (1) explain the rationale for, and the theories behind, the design of A Tailored Approach; (2) report the current conceptual illustration of A Tailored Approach and to describe a user’s interaction with the process model; and (3) situate the elements novel to A Tailored Approach (when compared with the Conventional Approach) in the relevant literature. A Tailored Approach suggests investing time at the start of a review, to develop the information needs from the research objectives, and to tailor the search approach to studies or data. Tailored Approaches should be led by the information specialist (librarian) but developed by the research team. The aim is not necessarily to focus on comprehensive retrieval. Further research is indicated to evaluate the use of supplementary search methods, methods of team-working to define search approaches, and to evaluate the use of conceptual models of information retrieval for testing and evaluation.
      Citation: Journal of Information Science
      PubDate: 2022-09-16T10:17:23Z
      DOI: 10.1177/01655515221114452
       
  • An intelligent system for multi-topic social spam detection in
           microblogging

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      Authors: Bilal Abu-Salih, Dana Al Qudah, Malak Al-Hassan, Seyed Mohssen Ghafari, Tomayess Issa, Ibrahim Aljarah, Amin Beheshti, Sulaiman Alqahtani
      Abstract: Journal of Information Science, Ahead of Print.
      The communication revolution has perpetually reshaped the means through which people send and receive information. Social media is an important pillar of this revolution and has brought profound changes to various aspects of our lives. However, the open environment and popularity of these platforms inaugurate windows of opportunities for various cyber threats, thus social networks have become a fertile venue for spammers and other illegitimate users to execute their malicious activities. These activities include phishing hot and trendy topics and posting a wide range of contents in many topics. Hence, it is crucial to continuously introduce new techniques and approaches to detect and stop this category of users. This article proposes a novel and effective approach to detect social spammers. An investigation into several attributes to measure topic-dependent and topic-independent users’ behaviours on Twitter is carried out. The experiments of this study are undertaken on various machine learning classifiers. The performance of these classifiers is compared and their effectiveness is measured via a number of robust evaluation measures. Furthermore, the proposed approach is benchmarked against state-of-the-art social spam and anomalous detection techniques. These experiments report the effectiveness and utility of the proposed approach and embedded modules.
      Citation: Journal of Information Science
      PubDate: 2022-09-15T07:25:10Z
      DOI: 10.1177/01655515221124062
       
  • Exploring hotspots and user information behaviour of WeChat official
           accounts: An empirical study based on stimulus–response model

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      Authors: Bo Yang, Jingyun Huang, Xusen Cheng, Shuyu Du
      Abstract: Journal of Information Science, Ahead of Print.
      The aim of this study was to investigate the hotspots of WeChat official accounts and the impact of their pushes on user information behaviour including reading rate, sharing rate, number of comments or collections and fan growth rate. Using nine official accounts provided by the Sootoo Network, this study collected data on more than 10,000 pushes released from January to December 2017. In this study, a second-order user information behaviour model using the collected data was constructed. Based on empirical research, a prediction model of user information behaviour was built using a backpropagation neural network and random forest algorithm, and two variable sets were used for training. Then, the effect of different prediction models was analysed to determine the main factors affecting user information behaviour. This study addresses gaps in the field of WeChat research, and the results are of great practical significance for the operators of WeChat official accounts: they can help them optimise operation effects and enhance the influence of official accounts.
      Citation: Journal of Information Science
      PubDate: 2022-09-13T06:37:52Z
      DOI: 10.1177/01655515221123962
       
  • Information behaviour in high risk decision making: Study of international
           postgraduates

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      Authors: Carolyn McNicholas, Rita Marcella
      Abstract: Journal of Information Science, Ahead of Print.
      This article explores the role of information in high risk consumer decision making. Forty-two qualitative interviews were undertaken with international non-EU postgraduates when making the high risk decision to study in a UK Business School. Prospective international postgraduates moved iteratively through the stages in Kuhlthau’s Information Search Process model and learnt from the search process they had undertaken in a continuous cyclical manner. Word-of-mouth recommendations were the most influential sources of information gathered, and online sources were perceived to be credible regardless of their origins. The perception of risk impacted the rigour of the information search process. An iterative decision making cycle model is proposed with Kuhlthau’s model and word of mouth information at its core, which reflects the connectedness of individuals in this digital era. This study provides new insights by combining both marketing and LIS models and extends Kuhlthau’s research into a new context.
      Citation: Journal of Information Science
      PubDate: 2022-09-13T06:36:52Z
      DOI: 10.1177/01655515221124080
       
  • Investigating the relationships between dialog patterns and user
           satisfaction in customer service chat systems based on chat log analysis

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      Authors: Tingting Jiang, Qian Guo, Yuhan Wei, Qikai Cheng, Wei Lu
      Abstract: Journal of Information Science, Ahead of Print.
      While previous studies of customer service chat systems (CSCS) understood user satisfaction as individuals’ subjective perceptions and depended heavily on self-report methods for satisfaction measurement, this article presents an obtrusive chat log analysis that followed the established approaches of search log analysis and examined the relationships between dialog patterns and user satisfaction. An 81-day chat log was obtained from a real-world CSCS that involves both a chatbot and human representatives. A total of 75,918 chat sessions/147,972 sub-sessions containing 251,556 user messages and 349,416 system messages were extracted after data processing and analysed in terms of topic, length and path. As found in this study, the users were more likely to get satisfied on low-difficulty topics. The dialog between the CSCS and users was shallow in general. While human representatives’ elaboration contributed to user satisfaction, the chatbot was responsible for damaging user satisfaction. The significance of this study consists not only in providing objective evidence about user satisfaction in online chat but also in generating practical implications for the CSCS to improve user satisfaction.
      Citation: Journal of Information Science
      PubDate: 2022-09-13T06:35:52Z
      DOI: 10.1177/01655515221124066
       
  • JSON document clustering based on schema embeddings

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      Authors: D Uma Priya, P Santhi Thilagam
      Abstract: Journal of Information Science, Ahead of Print.
      The growing popularity of JSON as the data storage and interchange format increases the availability of massive multi-structured data collections. Clustering JSON documents has become a significant issue in organising large data collections. Existing research uses various structural similarity measures to perform clustering. However, differently annotated JSON structures may also encode semantic relatedness, necessitating the use of both syntactic and semantic properties of heterogeneous JSON schemas. Using the SchemaEmbed model, this paper proposes an embedding-based clustering approach for grouping contextually similar JSON documents. The SchemaEmbed model is designed using the pre-trained Word2Vec model and a deep autoencoder that considers both syntactic and semantic information of JSON schemas for clustering the documents. The Word2Vec model learns the attribute embeddings, and a deep autoencoder is designed to generate context-aware schema embeddings. Finally, the context-based similar JSON documents are grouped using a clustering algorithm. The effectiveness of the proposed work is evaluated using both real and synthetic datasets. The results and findings show that the proposed approach improves clustering quality significantly, with a high NMI score of 75%. In addition, we demonstrate that clustering results obtained by contextual similarity are superior to those obtained by traditional semantic similarity models.
      Citation: Journal of Information Science
      PubDate: 2022-09-13T06:35:33Z
      DOI: 10.1177/01655515221116522
       
  • Discovering the fundamental strategic indicators of the use of Internet of
           Things in libraries: A grounded theory study

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      Authors: Ghasem Ali Ehsanian, Safiyeh Tahmasebi Limooni, Mitra Ghiasi
      Abstract: Journal of Information Science, Ahead of Print.
      The aim of this study is to identify the strategic indicators Internet of Things (IoT) application in libraries and to present a conceptual model. The research was performed qualitatively and the data method of the foundation. Data were collected through documentary methods and interviews with a statistical sample of 13 professors of information science and the snowball sampling method. Interview data were analysed in three steps of open, axial and selective coding. Validity assessment was determined by the responsive method and reliability of two coding tests. After analysing the findings, 35 main codes of subcategories and concepts were discovered. The main categories were divided into eight categories: control and supervision, providing advanced services, accessibility, intelligence, maintaining security, new thinking and development, information literacy and method of use and satisfaction, and based on this, a paradigmatic and theoretical model was presented. In the theoretical model of the main phenomenon, the strategic indicators are the use of IoT in libraries, and the classes and the main phenomenon are related to the main class of application strategies. Strategic indicators of IoT application are leading in the growth and development of libraries. These indicators are important in the IoT use in libraries and their development. The eight categories identified in the conceptual model were considered significant by the interviewees. Thus, the indicators discovered are effective and necessary in the success of libraries that use the IoT.
      Citation: Journal of Information Science
      PubDate: 2022-09-10T07:12:00Z
      DOI: 10.1177/01655515221123984
       
  • Multimodal sentiment analysis of intangible cultural heritage songs with
           strengthened audio features-guided attention

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      Authors: Tao Fan, Hao Wang
      Abstract: Journal of Information Science, Ahead of Print.
      Intangible cultural heritage (ICH) songs convey folk lives and stories from different communities and nations through touching melodies and lyrics, which are rich in sentiments. Currently, researches about the sentiment analysis of songs are mainly based on lyrics, audios and lyric-audio. Recent studies have shown that deep spectrum features extracted from the spectrogram, generated from the audio, perform well in several speech-based tasks. However, studies combining spectrum features in multimodal sentiment analysis of songs are in a lack. Hence, we propose to combine the audio, lyric and spectrogram to conduct multimodal sentiment analysis for ICH songs, in a tri-modal fusion way. In addition, the correlations and interactions between different modalities are not considered fully. Here, we propose a multimodal song sentiment analysis model (MSSAM), including a strengthened audio features-guided attention (SAFGA) mechanism, which can learn intra- and inter-modal information effectively. First, we obtain strengthened audio features through the fusion of acoustic and spectrum features. Then, the strengthened audio features are used to guide the attention weights distribution of words in the lyric with help of SAFGA, which can make the model focus on the important words with sentiments and related with the sentiment of strengthened audio features, capturing modal interactions and complementary information. We take two world-level ICH lists, Jingju (京剧) and Kunqu (昆曲), as examples, and build sentiment analysis datasets. We compare the proposed model with other state-of-the-arts baselines in Jingju and Kunqu datasets. Experimental results demonstrate the superiority of our proposed model.
      Citation: Journal of Information Science
      PubDate: 2022-09-09T06:53:12Z
      DOI: 10.1177/01655515221114454
       
  • A comprehensive bibliometric analysis on opinion mining and sentiment
           analysis global research output

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      Authors: Ibrahim Hussein Musa, Ibrahim Zamit, Kang Xu, Khaoula Boutouhami, Guilin Qi
      Abstract: Journal of Information Science, Ahead of Print.
      The rise of the Internet and social media (i.e. reviews, forum discussions, blogs and social networks) constituted an interesting source to detect user opinion trends. This study examines the global publication output on opinion mining and sentiment analysis from documents published in 2000 to 2020. Bibliometric indicators on the trends, most cited papers, authors, institutions, countries, funding agencies and research subject areas were independently screened and analysed using bibliometrix package in R. A total of 7603 eligible documents were identified from 2000 to 2020. The total number of citations for all publications was 129,251, with an average of 17.0 citations per publication. About 14,629 authors wrote those documents with 1.93 authors per document and a collaboration index of 1.98. The most prolific author was Cambria Erik, with 47 publications and h-index of 42. The leading countries for research were China with n = 824, India with n = 576 and the United States with n = 244 publications. Lecture Notes in Computer Science proceedings was the top-ranked venue for publications with n = 434, h-index of 32 and 4598 total citation scores. National Natural Science Foundation of China was the top-ranked funding agency for research, and most of the publications were computer science (n = 6320) documents. The study provides an in-depth assessment of the landmark of the hot research topic and acknowledges the contribution of the most productive and active authors across different countries in the world. In addition, the findings could support the younger scholars in their future research direction and improve the efficiency in potential future research collaborations and projects.
      Citation: Journal of Information Science
      PubDate: 2022-09-08T05:53:21Z
      DOI: 10.1177/01655515211061866
       
  • A novel integrated framework based on multi-view features for
           multidimensional social bot detection

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      Authors: Tingting Li, Ziming Zeng, Shouqiang Sun, Jingjing Sun
      Abstract: Journal of Information Science, Ahead of Print.
      The ravage of malicious bots in online social networks has profoundly affected normal users. In this article, we propose a novel integrated framework to detect social bots. Specifically, social bot detection is performed from two dimensions: binary-class and fine-grained detection. Moreover, 35 features from three views are extracted to detect social bots, including eight newly defined features. Then, a category balancing based on resampling technology is designed to balance the training data. Finally, a divide-and-conquer strategy is integrated into Random Forest, and the interference of noise in the training process is reduced. Feature effectiveness evaluation found that extracting features from multi-views can describe bots more comprehensively. It is also noted from the category imbalance test that the balanced data set can prevent the detection result from tilting. Comparative experiments show that the integrated framework is more effective than the baseline both in social bot detection and the type detection of bots.
      Citation: Journal of Information Science
      PubDate: 2022-09-07T07:34:26Z
      DOI: 10.1177/01655515221116517
       
  • Measuring knowledge contribution performance of physicians in online
           health communities: A BP neural network approach

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      Authors: Sudi Xia, Zhijian Zhang, Shaoxiong Fu, Xiaoyu Chen
      Abstract: Journal of Information Science, Ahead of Print.
      Extant literature on measuring the performance of physicians’ knowledge contribution in an online health community (OHC) is limited. To address this gap, this article aims to (1) develop a measurement model for physicians’ knowledge contribution performance; (2) use BP neural network to assign reasonable weight to each indicator of the model; and (3) explore the status and differences of knowledge contribution performance among a group of physicians. Based on the sample of 5407 infectious disease physicians in a Chinese OHC, we propose the measurement model by integrating physicians’ active knowledge contribution (AKC) and responsive knowledge contribution (RKC), covering 11 dimensions of contribution quantity and quality. We employ the BP neural network to optimise the model weights using the initial weight of the model obtained by the entropy method. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to evaluate the performance of physicians’ knowledge contribution in the OHC. The results show that it is feasible to use BP neural network to assign model weights. The distribution of physicians’ knowledge contribution performance is uneven; only a few have a high-level knowledge contribution performance. Meanwhile, a significant positive correlation exists between a physician’s title and respective knowledge contribution performance. Our research may contribute to related literature and practices by offering a fine-grained understanding of the performance of physicians’ knowledge contribution.
      Citation: Journal of Information Science
      PubDate: 2022-09-06T09:37:24Z
      DOI: 10.1177/01655515221121946
       
  • How do information overload and message fatigue reduce information
           processing in the era of COVID-19' An ability–motivation approach

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      Authors: Bingjing Mao, Xiaofeng Jia, Qian Huang
      Abstract: Journal of Information Science, Ahead of Print.
      The global outbreak of COVID-19 in 2020 has led to the dominance of COVID-19 prevention information on all media channels. Drawing on the ability–motivation model of information processing, this study examined how such an information overabundance hampered individuals’ ability and motivation to process in the era of COVID-19. With a survey conducted from 493 participants, we found that less message elaboration of COVID-19 prevention information was predicted by greater message fatigue, a state of low motivation due to information overabundance. In addition, greater message fatigue was accompanied by greater information overload, a state of low ability due to information overabundance. Moreover, certain motivation-related (i.e. health status, trait reactance and frequency of information seeking) and ability–related factors (i.e. health literacy, health status, trait anxiety and information quality) were found to be associated with message fatigue and information overload, respectively. The theoretical and practical implications are discussed.
      Citation: Journal of Information Science
      PubDate: 2022-09-05T07:22:47Z
      DOI: 10.1177/01655515221118047
       
  • Text-based experiment retrieval in genomic databases

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      Authors: Duygu Dede Şener, Hasan Ogul, Selen Basak
      Abstract: Journal of Information Science, Ahead of Print.
      With the growing number of genomic data in public repositories, efficient search methodologies have become a basic need to reach the relevant genomic data. However, this need cannot be fulfilled with the current repositories because they offer a limited search option which is a lexical matching of textual descriptions or metadata of the experiments. This technique is insufficient to get the required information needed to detect similarities between experiments within a large data collection. Due to the limitation of the existing repositories, in this study, we develop a text-based experiment retrieval framework by using both lexical and semantic similarity approaches to find similarities between experiments, and their retrieval performance was compared. This study is the first attempt to use text-driven semantic analysis approaches for developing a retrieval framework for experiments. An empirical study was conducted on a large textual description of Arabidopsis microarray experiments from the Gene Expression Omnibus database. In the proposed model, Jaccard similarity was used as a lexical similarity approach; Latent Semantic Analysis, Probabilistic Latent Semantic Analysis and Latent Dirichlet allocation were used as semantic similarity approaches to detect similarities between the textual descriptions of the experiments. According to the experimental results, relevant experiments can be retrieved successfully by text-driven semantic similarity approaches compared with the lexical similarity approach.
      Citation: Journal of Information Science
      PubDate: 2022-09-03T09:18:25Z
      DOI: 10.1177/01655515221118670
       
  • Components of reading culture: Insights from bibliometric analysis of
           1991–2020 research

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      Authors: Sanila Aslam, Saima Qutab, Nusrat Ali
      Abstract: Journal of Information Science, Ahead of Print.
      In the digital age, the reading habits are gradually declining and poor reading skills are causing various informational and social challenges include lack of critical thinking and intellectual irresponsibility. The main objective of this study is to consolidate the published works to comprehend the influencing factors of reading culture. We examine the reading skills, reading habits and reading behaviour literature from 1991 to 2020 to measure the scope and depth of reading culture (RC) through bibliometric analysis and visualisation by utilising a VOSviewer software based on Web of Science database. These selected 1139 articles are classified according to year of publication, author, the country of publications and author affiliation. The analysis provided insights about the RC publication trends as the most productive years are 2017 and 2019. The United States, Spain and China are the leading countries contributing to reading culture-related publications. Jyväskylä University and Florida State University serve as the most productive institutions. Furthermore, we also conduct a thematic analysis based on the keywords to identify the components and influencing factors contributing to the reading culture. The four main components of the reading culture are identified as reading skills, behaviour, habits and factors. At the end, we provided propositions to support the reading culture and future research directions for the interest of Library and Information Science (LIS) professionals, academicians and researchers.
      Citation: Journal of Information Science
      PubDate: 2022-09-03T09:14:02Z
      DOI: 10.1177/01655515221118667
       
  • Assessing children’s information and knowledge organisation competency
           in elementary schools of Hong Kong

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      Authors: Yejun Wu, Samuel KW Chu
      Abstract: Journal of Information Science, Ahead of Print.
      This article evaluates the information and knowledge organisation competency of third- to fifth-grade primary school students in Hong Kong directly or indirectly. The majority of the students are aged 8–11 years. The types of information and knowledge organisation schemes to be identified or organised include shallow taxonomies (e.g. a list of entities, a list of features of an entity, a list of events) and simple descriptive ontologies (e.g. a sequence of events, reasons of events, relation between entities or events). A total of 86 students participated in the study. Each student was asked to read an English book and a Chinese book, and to answer assessment questions about the content within the books. The questions ask children to identify members of a flat taxonomy and organise simple descriptive ontologies. The children’s overall information and knowledge organisation competency is found to be weak, but children’s information and knowledge organisation capabilities are not equally weak. The children identify features of an entity significantly better than a list of events, and identify reasons significantly better than flat taxonomies and relations. The findings have theoretical and practical implications for book writers, book cover designers, teachers, librarians and designers of information systems for children.
      Citation: Journal of Information Science
      PubDate: 2022-09-03T09:13:02Z
      DOI: 10.1177/01655515221118048
       
  • A formal study of co-opetition in scholarly publishing

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      Authors: Jose A García, Rosa Rodriguez-Sánchez, J Fdez-Valdivia
      Abstract: Journal of Information Science, Ahead of Print.
      In this article, we model and study a cooperative peer-review scenario in scholarly publishing. In this scenario, the peer-reviewed journals cooperate in the necessary investment for the peer-review system. However, the final decision on what to publish in each journal would rest with the journal’s editor, and the journals still compete in their quality standards for accepting papers. This simultaneously cooperative and competitive relationship between peer-reviewed journals is co-opetition in scholarly publishing. From the comparison between a benchmark scenario of competition between journals and a cooperative peer-review setting, we find that by sharing the cost of providing a common peer-review system, the peer-reviewed journals could offer a higher review quality in the manuscript evaluation process than they would otherwise be able to achieve individually. Furthermore, we find the conditions under which the competing academic journals using cooperative peer review could increase their expected quality levels, their standards for accepting articles, and their peer-review quality, which establishes the benefit from co-opetition between peer-reviewed journals. Nevertheless, a threshold cost-sharing factor exists above which the benefit from cooperative peer review disappears.
      Citation: Journal of Information Science
      PubDate: 2022-09-03T09:11:43Z
      DOI: 10.1177/01655515221116521
       
  • Why a user prefers an artwork: A deep attention model for artwork
           recommendation

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      Authors: Zhiqiang Tian, Yezheng Liu, Jianshan Sun, Xue Zhang, Yuanchun Jiang
      Abstract: Journal of Information Science, Ahead of Print.
      The combination of art market and emerging e-commerce has brought new trade opportunities and has achieved continuous growth in recent years. More and more people, especially young people, are keen to browse art information and buy artwork on the Internet. Therefore, designing an effective method of recommending artworks may not only effectively enhance the user experience in the art market but also benefit the greater transaction growth. The artwork recommendation task in e-commerce has not received much attention. Previous research works often regard the artworks as ordinary pictures and do not take into account the particularity of the artwork. To solve this problem, we modelled the aesthetic features into artwork recommendation and used the attention mechanism to learn user preferences for various features. We proposed a DAAR (Deep Attention Artwork Recommendation) model and used the attention mechanism to model the user’s preference weights for different features (including content features, aesthetic features and authors). To verify the validity of the proposed model, we collected data and conducted experiments on a real artwork community website. The experimental results show that the proposed DAAR model was better than the current state-of-the-art recommendation methods.
      Citation: Journal of Information Science
      PubDate: 2022-09-01T07:21:29Z
      DOI: 10.1177/01655515221116511
       
  • Towards a typology development of crowdsourcing in science

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      Authors: Regina Lenart-Gansiniec
      Abstract: Journal of Information Science, Ahead of Print.
      Crowdsourcing in science as collaborative online process through which non-professional and/or professional scientists incorporate a group of individuals of varying, diversity knowledge and skills, via an open call to the Internet and/or online platforms, to undertaking of a task in science, is an important strategy to support scientific research that has gained attention in academia and practitioners. While research efforts to date have focused on the benefits of crowdsourcing in science, its typology has yet to mature. Typologies are important in describing complex, multidisciplinary organisational forms such as crowdsourcing in science. The main purpose of this article is to identify and provide a typology of crowdsourcing in science. Based on the thematic analysis of publications collected in a systematic manner and focused group interviews, 12 types of crowdsourcing in science are identified. The proposed crowdsourcing in science typology matrix may be a starting point for future research and decision-making by practitioners regarding the choice of a specific type of crowdsourcing in science.
      Citation: Journal of Information Science
      PubDate: 2022-08-29T10:19:42Z
      DOI: 10.1177/01655515221118045
       
  • Knowledge as a theoretical object: Implications for knowledge management

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      Authors: William B Edgar, Kendra S Albright, Albina Krymskaya
      Abstract: Journal of Information Science, Ahead of Print.
      The thesis of this paper is that, despite its substantial accomplishments over the past 40 years as a professional practice, knowledge management (KM) has yet to mature as an intellectual discipline. The goal of this conceptual paper is to accelerate this maturation by generalizing KM. The paper does so by identifying KM’s generic social phenomena and how these phenomena can be better understood through the development of theory oriented around knowledge that defines, predicts, and explains them. KM theory is examined through an extensive review of the existing literature. Four levels of organizational KM are offered, including two that examine an organization’s basis (resources) and essence (ideas and actions), while the second two build upon these to examine resource management in general, and the management of knowledge resources specifically. Treating KM as a theoretical object will lead to greater understanding of KM. This enriched understanding will lead to a more effective performance of KM. The achievement of this paper’s goal (i.e., generalizing KM) will facilitate the maturation of KM into a discipline and practice that consistently contributes to organizational effectiveness. Little KM literature exists on the conceptualization of knowledge as a theoretical object, which offers the opportunity not only to develop theory that defines, predicts, and explains KM within organizations more systematically but also ultimately to understand better the ways in which KM contributes to individual happiness, community cohesion, and social prosperity.
      Citation: Journal of Information Science
      PubDate: 2022-08-29T10:18:02Z
      DOI: 10.1177/01655515221116555
       
  • Tweets on a horror movie: An investigation into relationships between
           sentiment strength, cognitive language and tweet virality

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      Authors: Ling Zhang, Xiangming Mu
      Abstract: Journal of Information Science, Ahead of Print.
      This article studies how sentiment strength and cognitive language may influence the levels of tweet virality. A total of 11,381 tweets about a horror movie (‘Mother!’) were collected. Based on the definitions of two independent variables: sentiment strength and cognitive language use, and the dependent variable: tweet virality, the data descriptive statistics and analysis of variance (ANOVA) analysis were applied to reveal the relationships between tweet virality and sentiment and cognitive factors. The results indicate that a high tweet virality is associated with either a lower level of sentiment strength or/and a higher level of cognitive language use by a statistically significant margin. This finding is more evident for negative tweets. The study findings help improve the understanding about sentimental and cognitive factors impacting tweet virality and guide the movie industry to improve marketing movie content to achieve high virality on social media. The conclusions can also be applied to other industries, government agencies, organisations and individuals who intend to quickly disseminate specific information on social media platforms.
      Citation: Journal of Information Science
      PubDate: 2022-08-29T10:16:21Z
      DOI: 10.1177/01655515221116516
       
  • Query expansion using Haar wavelet transform

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      Authors: Abhishek Kumar Shukla, Sujoy Das
      Abstract: Journal of Information Science, Ahead of Print.
      Novice users are unable to express their information needs properly, due to this it is difficult to retrieve all the desired relevant documents from the test collection. The problem of word mismatch is fundamental to information retrieval. Query expansion is a technique in which additional terms are added to retrieve relevant documents. In this article, we have expanded the query using pseudo-relevance feedback and Haar wavelet transform. The performance of the proposed technique is evaluated on FIRE 2011 ad hoc English test Collection and Robust dataset. The mean average precision of the proposed model on the FIRE dataset and Robust Track dataset is 0.3334 and 0.2724, respectively.
      Citation: Journal of Information Science
      PubDate: 2022-08-29T10:13:22Z
      DOI: 10.1177/01655515221111005
       
  • Extractive text summarisation using Bayesian state estimation of
           sentences: A Markovian framework

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      Authors: Saba Ghanbari Haez, Farhad Shamsfakhr
      Abstract: Journal of Information Science, Ahead of Print.
      Identifying and extracting valuable information from textual documents in the form of cohesively and appropriately developed summaries is one of the most challenging tasks in text mining and natural language processing. In this article, we present a sequential Markov model, equipped with Bayesian inference, to estimate the degree of importance of sentences in a document and thereby address the text summarisation problem. The proposed methodology models the extractive sentence summarisation as a Bayesian state estimation problem, where the system state is the importance degree of each sentence in a document. The transition and observation models are derived using a nonlinear dynamical system identification based on a recurrent feedback neural model that predicts the sentence observation using the sentence input data. In the end, the transition and observation probability density functions are modelled using a mixture density network. The performance assessment of the system has been carried out by investigating the optimal feature dimensionality and the impact of the model parameters on the system accuracy, using entropy-based risk and loss-based risk measures. Finally, the superiority of the proposed methodology over the state of the art in extractive summarisation is discussed and verified by reporting the recall, precision and accuracy on the real-world benchmark data sets.
      Citation: Journal of Information Science
      PubDate: 2022-08-27T09:38:06Z
      DOI: 10.1177/01655515221112842
       
  • Students’ perception of academic databases as recognition of learning
           and research during the COVID-19 pandemic

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      Authors: Hua Du, Hongyan Chen, A Y M Atiquil Islam
      Abstract: Journal of Information Science, Ahead of Print.
      Consistent with their goal of becoming a centre for educational excellence in teaching, learning and research, the authorities of Chinese higher education introduced their academic database system as one of their e-library services. However, the existing literature exhibits inadequate empirical measurements of academic databases in all aspects of higher education throughout the world during the COVID-19 pandemic. To address this gap, this study aims to validate the technology satisfaction model (TSM) for measuring students’ satisfaction in using academic databases for their learning and research purposes. This study also analysed local and international academic databases to explore whether these databases could play a moderating role in shaping learners’ satisfaction. The data were collected through a survey of 500 respondents studying at a research university in Shanghai. The results, which were analysed by structural equation modelling and the Rasch model, showed that students’ satisfaction is determined by three valid predictors: computer self-efficacy, perceived usefulness and ease of use, and causal direct and indirect relationships among these variables in the use of local and international academic databases. Our new findings on the moderating effect of local (i.e. Chinese) and international (e.g. English) academic databases highlighted that the TSM has successfully estimated dual databases and produced insignificant, dissimilar results. This study could aid local and international educators, researchers, information science professionals and others in measuring the perception of academic databases for learning and research. This research could also serve as a guideline for researchers and psychometricians in measuring innovative learning technologies using structural equation modelling and the Rasch model. This is the unique contribution of this study, which concludes that local and international academic databases are almost equally important for postgraduate students at a research university in China. Moreover, these students are satisfied in using these academic databases to learn and do research.
      Citation: Journal of Information Science
      PubDate: 2022-08-20T06:23:47Z
      DOI: 10.1177/01655515221118666
       
  • Algorithm metadata vocabulary: A representational model and metadata
           vocabulary for describing and maintaining algorithms

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      Authors: Biswanath Dutta, Jyotima Patel
      Abstract: Journal of Information Science, Ahead of Print.
      Metadata vocabularies are used in various domains of study. It provides an in-depth description of the resources. In this work, we develop algorithm metadata vocabulary (AMV), a vocabulary for capturing and storing the metadata about the algorithms (a procedure or a set of rules that is followed step-by-step to solve a problem, especially by a computer). The snag faced by the researchers in the current time is the failure of getting relevant results when searching for algorithms in any search engine. The designed vocabulary can be used by the algorithm repository developers, managers, and application developers. Besides, AMV is represented as a semantic model and produced OWL file, and it can be directly used by anyone interested to create and publish algorithm metadata as a knowledge graph, or to provide metadata service through the SPARQL endpoint. To design the vocabulary, we propose a well-defined methodology, which considers factual issues faced by the algorithm users and the practitioners. The evaluation shows promising results.
      Citation: Journal of Information Science
      PubDate: 2022-08-16T09:30:48Z
      DOI: 10.1177/01655515221116557
       
  • Investigating community evolutions in TikTok dangerous and non-dangerous
           challenges

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      Authors: Gianluca Bonifazi, Silvia Cecchini, Enrico Corradini, Lorenzo Giuliani, Domenico Ursino, Luca Virgili
      Abstract: Journal of Information Science, Ahead of Print.
      In just few years, TikTok has become a major player in the social media environment, especially with regard to teenagers. One of the key factors of this success is the idea of challenges, that is, video competitions/emulations on a certain topic, which a user can launch and other ones can join. Most of the challenges are fun and harmless. However, there are also users who launch challenges that are dangerous, or at least suitable only for an adult audience (and TikTok is the most popular social network for teenagers). This article focuses primarily on this kind of challenge. In particular, it investigates an aspect not yet studied in the literature, which is the different characteristics and evolutionary dynamics of the communities of users participating in non-dangerous and dangerous challenges. Its final goal is the identification of evolutionary patterns that distinguish the communities of users participating in the two types of challenges. The knowledge of these patterns could be a first step in implementing an approach to the early detection of dangerous challenges in TikTok.
      Citation: Journal of Information Science
      PubDate: 2022-08-12T08:57:09Z
      DOI: 10.1177/01655515221116519
       
  • Finding answers to COVID-19-specific questions: An information retrieval
           system based on latent keywords and adapted TF-IDF

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      Authors: Jorge Chamorro-Padial, Francisco-Javier Rodrigo-Ginés, Rosa Rodríguez-Sánchez
      Abstract: Journal of Information Science, Ahead of Print.
      The scientific community has reacted to the COVID-19 outbreak by producing a high number of literary works that are helping us to understand a variety of topics related to the pandemic from different perspectives. Dealing with this large amount of information can be challenging, especially when researchers need to find answers to complex questions about specific topics. We present an Information Retrieval System that uses latent information to select relevant works related to specific concepts. By applying Latent Dirichlet Allocation (LDA) models to documents, we can identify key concepts related to a specific query and a corpus. Our method is iterative in that, from an initial input query defined by the user, the original query is expanded for each subsequent iteration. In addition, our method is able to work with a limited amount of information per article. We have tested the performance of our proposal using human validation and two evaluation strategies, achieving good results in both of them. Concerning the first strategy, we performed two surveys to determine the performance of our model. For all the categories that were studied, precision was always greater than 0.6, while accuracy was always greater than 0.8. The second strategy also showed good results, achieving a precision of 1.0 for one category and scoring over 0.7 points overall.
      Citation: Journal of Information Science
      PubDate: 2022-08-12T06:01:49Z
      DOI: 10.1177/01655515221110995
       
  • DRMM: A novel data mining-based emotion transfer detecting method for
           emotion prediction of social media

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      Authors: Wei Shi, Guangcong Xue, Xicheng Yin, Shaoyi He, Hongwei Wang
      Abstract: Journal of Information Science, Ahead of Print.
      With the progress of the Internet and information technology, emotion analysis has been applied to analyse the emotional orientation and evolution trend of online public opinion of online tweets. At present, most of the existing methods use econometric model and machine learning algorithm to predict the trend of online public opinion. Although these methods have achieved good prediction results, they do not take into account the influence of internal factors on network public opinion prediction, such as mutual migration among emotion classes. The emotion may change dynamically because different events trigger it in the evolution process. In this view, this article proposes a novel method, called Deviation Rule Markov Model (DRMM), to predict the emotional change trend of Internet users in online public opinion by analysing the correlation between Internet users’ emotional categories. Structurally, the proposed DRMM involves various processes such as pre-processing, emotion classification, data mining and transfer prediction. For the processing of network comment data, the proposed model initially undergoes pre-processing to delete unnecessary data. Then, the extended fuzzy emotion ontology is used to annotate the emotion class of the comment data. Besides, an extended association rule mining algorithm is used in the emotion association analysis process to obtain the transfer probability between emotion classes. Moreover, Markov chain is used to construct an emotional state transition matrix to predict the transition probability of positive or negative emotions. According to the predicted single emotion transfer probability results, the analytic hierarchy process is used to assign values to different emotion classes, and finally, the transfer probability of the overall emotion in a certain period is obtained. Compared with the actual case, the mean absolute error (MAE) and root mean square error (RMSE) of the proposed model are 2.7119 and 3.7254, respectively, which has good prediction performance.
      Citation: Journal of Information Science
      PubDate: 2022-08-11T12:46:22Z
      DOI: 10.1177/01655515221100728
       
  • The effect of co-opinion on the cocitation-based information retrieval
           systems’ effectiveness evaluated by semantic similarity

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      Authors: Maryam Yaghtin, Hajar Sotudeh, Alireza Nikseresht
      Abstract: Journal of Information Science, Ahead of Print.
      The co-opinionatedness measure, that is, the similarity of cociting documents in their opinions about their cocited articles, has been recently proposed. The present study uses a wider range of baselines and benchmarks to investigate the measure’s effectiveness in retrieval ranking that was previously confirmed in a pilot study. A test collection was built including 30 seed documents and their 4702 cocited articles. Their citances and full-texts were analysed using natural language processing (NLP) and opinion mining techniques. Cocitation values, syntactical similarity and contexts similarity were used as baselines. The distributional semantic similarity and the linear and hierarchical Medical Subject Headings (MeSH) similarities served as benchmarks to evaluate the effect of the co-opinionatedness as a boosting factor on the performance of the baselines. The improvements in the rankings were measured by normalised discounted cumulative gain (nDCG). According to the findings, there existed significant differences between the nDCG mean values obtained before and after weighting the baselines by the co-opinionatedness measures. The results of the generalisability study corroborated the reliability and generalisability of the systems. Accordingly, the similarity in the opinions of the cociting papers towards their cocited articles can explain the cocitation relation in the scientific papers network and can be effectively utilised for improving the results of the cocitation-based retrieval systems.
      Citation: Journal of Information Science
      PubDate: 2022-08-08T12:01:48Z
      DOI: 10.1177/01655515221116518
       
  • Node classifications with DjCaNE: Disjoint content and network embedding

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      Authors: Mohsen Fazaeli, Saeedeh Momtazi
      Abstract: Journal of Information Science, Ahead of Print.
      Machine learning approaches have become a crucial tool in graph analysis. Despite the accurate results of the existing approaches, most of them are not scalable enough to be used in real-world problems. Networks provide two different kinds of information, nodes contents and nodes relations (network structure). Training deep graph neural networks (GNN) over large-scale graphs is challenging due to the limitation of the message passing framework. Graph Convolutional Networks (GCN) work on all node neighbours at once. Furthermore, it is usual to transform node features with a deep neural network before the GC operation. Therefore, the deep transform operation may apply up to hundreds of times for each target node which is heavy computation and hard to batch. This paper presents an abstract framework with two embedding components, the first component embeds node relations, and the second one embeds node contents. The model makes predictions by aggregating these embeddings through a combination component. The presented approach limits the deep transform only to the target node and uses random walk-based embedding instead of the GC operator to reduce the cost. The main goal of the proposed approach is to provide a light framework for the task. To this aim, node relations are embedded based on node neighbourhood structure by a biased variant of the DeepWalk model, called GuidedWalk, and an autoencoder embeds node contents. The experimental results on three well-known datasets show the superiority of the proposed model compared to the state-of-the-art GraphSAGE and TADW models with less computational complexity. On the Citeseer, Cora, and PubMed datasets, the model has achieved 3.23%, 0.88%, and 7.63% improvement in Macro-F1 and 3.25%, 0.7%, and 6.34% improvement in Micro-F1, respectively. Although GNNs are state-of-the-art models, considering node content is their main advantage. This paper shows that even a simple integration of node content to available random walk-based methods improves their performance up to GCNs without increasing the complexity.
      Citation: Journal of Information Science
      PubDate: 2022-08-08T11:59:28Z
      DOI: 10.1177/01655515221111002
       
  • A modified LSTM network to predict the citation counts of papers

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      Authors: Wumei Du, Zhemin Li, Zheng Xie
      Abstract: Journal of Information Science, Ahead of Print.
      Quantifiable predictability in the citation counts of articles is significant in scientometrics and informetrics. Many metrics based on the citation counts can evaluate the scientific impact of research articles and journals. Utilising time series models, an article’s citation counts up to the yth year after publication can be predicted by those up to the previous years. However, the typically used models cannot predict the fat tail of the actual citation distributions. Thus, based on cumulative advantage of the citation behaviour, we propose a method to predict the accumulated citation counts, by using a random number sampled from a power-law distribution to modify the results given by a recurrent neural network (RNN), long short-term memory. Extensive experiments on the data set including 17 journals in information science verified the effectiveness of our method by the good fittings on distributions and evolutionary trends of the citation counts of articles. Our method has the potential to be extended to predict other popular assessment measures such as impact factor and h-index for journals.
      Citation: Journal of Information Science
      PubDate: 2022-08-08T09:34:15Z
      DOI: 10.1177/01655515221111000
       
  • Examination of digital citizenship, online information searching strategy
           and information literacy depending on changing state of experience in
           using digital technologies during COVID-19 pandemic

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      Authors: Ümmühan Avcı, Hatice Yıldız Durak
      Abstract: Journal of Information Science, Ahead of Print.
      As educational processes are adapted to the online environment due to COVID-19 pandemic, digital citizenship and online information searching strategies came into prominence. In this context, the aim of this study is the examination of digital citizenship awareness, online information searching strategies and differentiation of the level of information literacy depending on changing state of experience in using digital technologies before and during COVID-19 pandemic. Also, it is aimed to examine the presence of relations between variables of the research. The study was conducted with the participation of 255 university students. Analyses were performed using multivariate analysis of variance (MANOVA) and partial least squares structural equation modelling (PLS-SEM). According to the results of the study, during COVID-19 pandemic, there was an increase in digital citizenship awareness of students who spend more time in social media and the ones who improved digital technology usage skills developed online information searching strategies. According to the model analysed, online information searching strategies and information literacy have an effect on digital citizenship while online information searching strategies have an effect on information literacy. The results of the study showed that the students with higher level of information literacy and online information searching strategies may help them develop digital citizenship awareness.
      Citation: Journal of Information Science
      PubDate: 2022-08-06T09:42:47Z
      DOI: 10.1177/01655515221114455
       
  • Comprehensive selective improvements in agri-informatics semantics

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      Authors: Muhammad Ishaq, Abdullah Khan, Muhammad Asim, Asfandyar Khan, Javed Iqbal Bangash
      Abstract: Journal of Information Science, Ahead of Print.
      The advent of information technology re-innovates all sectors of bio-sciences. Researchers use Semantic Web to improve web searching, mining and integration, which alleviates the time-consuming task of finding relevant and high-quality content. Semantics is improved through ontology engineering in any domain. Amended and developed ontologies will be uploaded to existing standardised and approved biomedical repositories. The establishment of a World Wide Web Consortium (W3C) approved and standardised ontology repository is the most ambitious goal. This work will solely focus on some selected agri-ontologies. The main objective is to promote outcome-based research and transformation styles of relevant expertise sharing. The intended goal is to win project funding to train and equip students with relevant skills and expertise. Need-based and market-oriented training and professional grooming are a tangible asset for students. The majority of traditional Web development freelancers are unaware of ontology or semantic web market demand. Freelancing is another option for expert Ontology developers. However, agriculture students are used to all the research vocabulary and terminologies in their area, but they do not know how to contribute their expertise to improve the efficiency of the Semantic Web in their domain. If the improvement in relevant ontology becomes a part of the Semantic Web, then it is termed ‘Real-time Web semantics enhancement’. In other words, the target ontology becomes a part of the future Web of meaning.
      Citation: Journal of Information Science
      PubDate: 2022-08-04T12:23:07Z
      DOI: 10.1177/01655515221110987
       
  • The use of subject headings varied in Embase and MEDLINE: An analysis of
           indexing across six subject areas

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      Authors: Tove Faber Frandsen, Anne-Marie Fiala Carlsen, Mette Brandt Eriksen
      Abstract: Journal of Information Science, Ahead of Print.
      Many bibliographic databases describe the content of a publication using a thesaurus. The vocabularies vary and the extent to which the databases apply them may also differ significantly. The aim of this study is to empirically explore the number of subject headings assigned to publications in two databases over time and to determine if publication characteristics are associated with the number of subject headings. Articles and reviews in MEDLINE and Embase from 1990 to 2019 assigned with one of the subject headings from six subject areas are included in this study. Each of the retrieved publications in Embase is matched with a similar publication in MEDLINE. Furthermore, multivariable linear regressions are used to explore the association of the number of subject headings in MEDLINE and Embase with six prespecified publication characteristics. The average number of assigned subject headings in MEDLINE is stable or even slightly decreasing over time. In Embase, the average number of assigned subject headings was stable until about 2000 where the average number increased dramatically during the next 3 years. Furthermore, linear regressions show that the average number of subject headings in MEDLINE and Embase is higher for publications in English, publications with longer abstract, recent publications and if it belongs to specific subject areas. However, reviews are assigned with more subject headings in Embase and fewer in MEDLINE. The implications of the results are discussed.
      Citation: Journal of Information Science
      PubDate: 2022-08-04T12:21:27Z
      DOI: 10.1177/01655515221107335
       
  • Inequality of authors’ reference reuse

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      Authors: Dan Wang, Yong Huang, Liang Hu, Qikai Cheng, Yi Bu
      Abstract: Journal of Information Science, Ahead of Print.
      This brief communication finds a clear and universal inequality of authors’ reference reuse behaviour. We observe that a few references are reused many times in an author’s oeuvre while most of his or her references only occur in the reference list for quite a limited number of times. A power law distribution depicts such an inequality. We particularly utilise the power value, [math], to characterise the nuanced difference of such inequalities. A pilot study based upon Microsoft Academic Graph (MAG) shows that the [math] tends to be normally distributed, regardless of whether it is from a citing or a cited perspective. Our empirical study also reveals that the [math] of highly cited publications tends to be greater than that of lowly cited ones, yet we also observe a saturation when the number of citations increases.
      Citation: Journal of Information Science
      PubDate: 2022-07-23T09:19:16Z
      DOI: 10.1177/01655515221111062
       
  • Considerations in releasing public data: The case of local governments in
           Korea

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      Authors: Chaeeun Song, Haklae Kim
      Abstract: Journal of Information Science, Ahead of Print.
      Local governments play a very important role in providing and disseminating public data; with the assistance of the national government, they also strive to promote effective policies for accessing this data. However, local governments face challenges in independently promoting public data policies owing to budget, manpower and technical constraints. This study analyses public data provided by local governments in South Korea from the perspective of management and use, apart from suggesting considerations for the public data policies of local governments. Public data provided by local governments were also collected, and both the data management method and use of such data were evaluated. Data management measures the currentness in complying with the update policy for each dataset, and the data utilisation measures the relationship between view and download as usefulness. A clustering analysis was conducted to analyse the common characteristics of individual local governments. According to the research results, most local governments do not systematically manage their datasets, and the use of the data provided is extremely low. It is therefore necessary to establish an effective data policy that considers the characteristics of local governments.
      Citation: Journal of Information Science
      PubDate: 2022-07-21T12:27:48Z
      DOI: 10.1177/01655515221106636
       
  • Improved PageRank and New Indices for Academic Impact Evaluation Using AI
           Papers as Case Studies

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      Authors: Rui Wang, Shijie Li, Qing Yin, Ji Zhang, Rujing Yao, Ou Wu
      Abstract: Journal of Information Science, Ahead of Print.
      Evaluating academic papers and groups is important in scholar evaluation and literature retrieval. However, current evaluation indices, which pay excessive attention to the citation number rather than the citation importance and unidirectionality, are relatively simple. This study proposes new evaluation indices for papers and groups. First, an improved PageRank (PR) algorithm introducing citation importance is proposed to obtain a new citation-based paper index (CPI) via a pre-ranking and fine-tuning strategy. Second, to evaluate the paper’s influence inside and outside its research field, the focus citation-based paper index (FCPI) and diversity citation-based paper index (DCPI) are proposed based on topic similarity and diversity, respectively. Third, aside from the statistical indices for academic papers, we propose a foreign academic degree of dependence (FAD) to characterise the dependence between two academic groups. Finally, artificial intelligence (AI) papers from 2005 to 2019 are utilised for a case study.
      Citation: Journal of Information Science
      PubDate: 2022-07-21T10:36:02Z
      DOI: 10.1177/01655515221105038
       
  • Anatomising the impact of ResearchGate followers and followings on
           influence identification

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      Authors: Mitali Desai, Rupa G Mehta, Dipti P Rana
      Abstract: Journal of Information Science, Ahead of Print.
      Influence analysis, derived from Social Network Analysis (SNA), is extremely useful in academic literature analytic. Different Academic Social Network Sites (ASNS) have been widely examined for influence analysis in terms of co-authorship and co-citation networks. The impact of other network-based features, such as followers and followings, provided by ASNS such as ResearchGate (RG) and Academia is yet to be anatomised. As proven in ingrained social theories, the followers and followings have significant impact in influence prorogation. This research aims at examining the same in one of the widely adopted ASNS, RG. The rendering process is developed to render real-time RG information, which is modelled into graph. Standard centrality measures are implemented to identify influential users from the constructed RG graph. Each centrality measure gives a list of top-k influential RG users. The results are compared with RGScore and Total Research Interest (TRI) to discover the most effective centrality measure. Betweenness and closeness centrality measures have shown the outperforming results compared with others. A procedure is established to discover influential RG users that are commonly present in all top-k centrality results to identify dominant skills, affiliations, departments and locations from the rendered data.
      Citation: Journal of Information Science
      PubDate: 2022-07-21T10:35:34Z
      DOI: 10.1177/01655515221100716
       
  • Gender inequality in applying research project and funding

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      Authors: Chien Hsiang Liao, Jiunn-Woei Lian
      Abstract: Journal of Information Science, Ahead of Print.
      Gender equality in scientific research has gradually attracted attention from many countries. However, the possible interaction effect of gender on research funding has not been fully disclosed. This study conducts an empirical study to examine the possible influences of gender on research funding and its interaction effect with applicants’ social influences, including their external cumulative advantages (Matthew effect) and internal cognition (halo effect). In total, 1465 research projects from 2015 to 2021 are analysed to examine the proposed hypotheses. The results reveal that there is no gender inequality in the association between the Matthew effect and future research funding. However, the halo effect is easily affected by gender. For male scientists, higher institutional reputation and past research performance lead to higher future research funding. However, female scientists have no such benefits. According to the findings, this study suggests that female scientists should give priority to accumulating their own external resource advantages and participate in academic activities more frequently to activate women’s participation in scientific research and academia.
      Citation: Journal of Information Science
      PubDate: 2022-07-18T10:33:43Z
      DOI: 10.1177/01655515221097861
       
  • Breakthrough potential of emerging research topics based on citation
           diffusion features

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      Authors: Haiyun Xu, Jos Winnink, Hongshen Pang, Shuhao Wen, Liang Chen
      Abstract: Journal of Information Science, Ahead of Print.
      This article uses the characteristics of citation curves in emerging research topics (ERTs) and combines them with the ERTs’ knowledge bases to draw conclusions by comparing their development patterns. The goal of this study is to enrich the toolset for predicting breakthroughs in scientific research. A set of multidimensional and practical bibliometric indicators is used to identify ERTs, to further identify the knowledge bases of ERTs and construct citation curves for both ERTs and their knowledge bases. The development trends of the citation curves of ERTs and their knowledge bases in different time periods are compared and analysed from two dimensions: knowledge transition and continuous growth. We use the field of stem cell research to test our method. Based on the outcome of the analysis, we can assess the breakthrough potential of ERTs. The stratification, transition and recent changes of the citation curve can be used as a basis for analysing and assessing the ERTs’ breakthrough potential. The combination of different citation diffusion patterns of ERTs and their knowledge bases can improve the effectiveness of identifying ERTs that can become breakthrough innovations.
      Citation: Journal of Information Science
      PubDate: 2022-07-14T04:29:50Z
      DOI: 10.1177/01655515211061219
       
  • Disambiguating the definitions of the concept ‘transformative
           innovation’

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      Authors: Haiyun Xu, Hongshen Pang, Jos Winnink, Rui Luo, Chao Wang
      Abstract: Journal of Information Science, Ahead of Print.
      The definition of ‘transformative innovation’ is still ambiguous, making it difficult to develop more targeted strategies for steering scientific and technological innovation. In this study, taking extant academic publications as our research object, we used topic extraction and visualisation tools to explore the intersections and differences among transformative and other innovative concepts. The correlation degrees among the concepts related to ‘transformative innovation’ were used to distinguish the relationships among concepts related to ‘breakthrough innovation’ and ‘disruptive innovation’. We further analysed the definitional differences among ‘transformative innovation’, ‘emerging technology’ and ‘groundbreaking research’. The results showed that the concepts of ‘breakthrough innovation’ and ‘disruptive innovation’ can be integrated into the scope of ‘transformative innovation’. However, the definitions of ‘transformative innovation’, ‘emerging technology’ and ‘research frontier’ had their distinct characteristics. ‘Transformative innovation’ focused on major impactful technological changes. ‘Emerging technology’ focused on novel technology and its promoting. ‘Research frontier’ focused on research activities taking place at the frontiers of knowledge. Therefore, distinctive science and technology (S&T) policies are needed for the different types of innovation. This article introduces a novel multidimensional method to place the different concepts associated with ‘transformative innovation’ into a unified framework. This framework is expected to support policymakers in their S&T policymaking, and it would aid the work of S&T researchers.
      Citation: Journal of Information Science
      PubDate: 2022-07-14T04:29:49Z
      DOI: 10.1177/01655515211061865
       
  • The information behaviour of individuals changing health insurance plans
           and an exploration of health insurance priorities

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      Authors: Emily Vardell, Ting Wang
      Abstract: Journal of Information Science, Ahead of Print.
      This study investigated why individuals change their health insurance plans, factors that influence their health insurance plan choices and information sources used to compare and select their desired plans. Semi-structured interviews and card sorting exercises with state university employees in the Midwest region were performed. Saving money was the main reason for switching health insurance plans. Health insurance plan coverage and cost, past experiences with the plans and coverage, health saving accounts, personal and/or family health status and forecasting health care demands for the upcoming year determined their choice of health insurance plan. Human Resource departments, printed materials, health insurance companies, online tools for comparing plans and interpersonal communications were the primary information sources for comparing and selecting health insurance plans. The study suggests that although individuals evaluate various factors and refer to multiple information sources when choosing a plan, they still experience uncertainty regarding selected plans for the coming year.
      Citation: Journal of Information Science
      PubDate: 2022-07-13T10:42:57Z
      DOI: 10.1177/01655515221108686
       
  • China’s policy similarity evaluation using LDA model: An experimental
           analysis in Hebei province

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      Authors: Junhuan Zhang, Wanbing Gui, Jiaqi Wen
      Abstract: Journal of Information Science, Ahead of Print.
      This article proposes a combination model, which is composed of latent Dirichlet allocation model, TF-IDF feature extraction algorithm and Euclidean distance measurement method, to identify and judge whether the similarities between multiple policy texts exist or not. With the help of actual data result, this will drive the relevant government agencies to figure out problems in a timely manner and provide a decision-making basis for them to formulate and optimise appropriate economic policies. To this end, this article analyses and studies the four types of economic texts that are classified as Insurance, Banking, Tax and Finance from the Central Government of Hebei province and Shijiazhuang city levels. Also, we consider Beijing, Shanghai and Guangdong. Experimental results show that (1) the combination model can quickly and effectively recognise and determine whether there are similarities between multiple economic policy texts; (2) similarities exist or not between the central, provincial and municipal level policy texts depending on the comparison of the distance values across them; (3) the smaller the distance value between economic policy texts of the same kind, the higher the similarity in them; and (4) the distance values between the six policy texts in Finance, Insurance, Bank and Tax categories are ranked from low to high. In terms of similarity, the Finance category is the highest, followed by Insurance and Bank, and the Tax category is the lowest.
      Citation: Journal of Information Science
      PubDate: 2022-07-12T12:54:45Z
      DOI: 10.1177/01655515221097858
       
  • Modelling the effect of perceived organisational policies on knowledge
           management in libraries: Focus on the moderating role of transformational
           leadership and professional commitment

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      Authors: Mojtaba Kaffashan Kakhki, Nargess Malakooti Asl, Ehsan Namdar Joyame
      Abstract: Journal of Information Science, Ahead of Print.
      The purpose of this study was to examine the effect of perceived organisational policies (POPs) on knowledge management (KM) with regard to the moderating role of transformational leadership (TL) and librarians’ professional commitment. The study was conducted in three stages. First, the research moderating variables were chosen through an explorative study and surveying the librarians. Following the design of the theoretical model, the Delphi method was employed to validate it. Finally, the model was tested with a sample of 205 librarians working at Iranian state universities. To examine the causal relationships between the research instrument variables, the structural equation modelling technique and Smart PLS software were used. The results of the study revealed that POP was moderated through TL and the librarians’ professional commitment and affected the processes of knowledge acquisition and identification, knowledge generation and sharing, and knowledge application. The results, further, confirmed the strong effect of POP on professional commitment and the effect of TL on KM. The findings indicate that moderating the negative effects of POP in academic libraries could pave the way for the improvement of librarians’ job performance. To date, no empirical investigations have examined the effect of POP on KM in libraries with regard to the moderating role of TL and the librarians’ professional commitment. This study is assumed to fill this gap.
      Citation: Journal of Information Science
      PubDate: 2022-07-12T12:52:45Z
      DOI: 10.1177/01655515221096330
       
  • Korea’s national approach to Open Science: Present and possible
           future

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      Authors: Hanna Shmagun, Jangsup Shim, Kwang-Nam Choi, Suk Kyung Shin, Jaesoo Kim, Charles Oppenheim
      Abstract: Journal of Information Science, Ahead of Print.
      Open Science (OS) – an emerging global trend driven by advances in digital technologies and government’s commitment to greater transparency and value for money of publicly funded research – is at its early stages, even in countries with high R&D expenditures, such as South Korea. This study provides a comprehensive overview and analysis of Korea’s national OS approach, with a focus on exploring the current OS regulatory and technological environments it operates under, and uncovering its SWOT – strengths, weaknesses, opportunities and threats. It concludes that internal weaknesses, such as insufficient political will to promote OS, dominate other SWOT characteristics of Korea’s national OS approach. Thus, the highest priority should be given to strategies attempting to minimise both internal weaknesses and external threats, such as reinforcing domestic Open Access publishing ecosystem to mitigate Korean researchers’ dependency on large international commercial publishers.
      Citation: Journal of Information Science
      PubDate: 2022-07-12T01:02:51Z
      DOI: 10.1177/01655515221107336
       
  • Professional benefits and challenges of health information documentation
           on Instagram for Iranian physicians

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      Authors: Hossein Ghalavand, Sirous Panahi, Shahram Sedghi, Saeid Shirshahi
      Abstract: Journal of Information Science, Ahead of Print.
      Social media are new tools that can be used for facilitating health information management. However, social media’s benefits and challenges for information documentation have not been identified well. This study sought to identify the benefits and challenges of Instagram to information documentation based on Iranian physicians’ viewpoints. In this qualitative study, a semi-structure interview was administered to 28 Iranian physicians, and data were collected using purposive and snowball sampling method. The data analysis was done using the thematic analysis method by MAXQDA 10. Based on the findings of this study, six benefits for information documentation over Instagram were identified include sharing lessons learned, developing incidental learning, empowering communications, supporting decision-making, managing personal brand and translating knowledge. Also, based on Iranian physicians’ viewpoints, three challenges were found for information documentation in Instagram include ethical and privacy challenges, poor information quality and damaging professional image. Iranian physicians have different attitudes towards using Instagram for information documentation. For control challenges, regulatory and security issues must be addressed to protect physicians’ privacy and more education is required for the health professionals to make them more aware of the nature of using social media.
      Citation: Journal of Information Science
      PubDate: 2022-07-12T01:00:51Z
      DOI: 10.1177/01655515221097860
       
  • People’s perceptions on social media archiving by the National
           Library of Japan

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      Authors: Ryo Shiozaki
      Abstract: Journal of Information Science, Ahead of Print.
      Social media content can be considered an unprecedented historical resource that reflects present-day ordinary life. However, although private data are publicly available on social media, the preserving of such personal content by a third party entails legal and ethical concerns. We report on a nationwide questionnaire survey conducted to obtain the responses of people to hypothetical scenarios of social media archiving by the National Diet Library in Japan. Within our survey sample, 35% of respondents (n = 1126) disagreed with scenarios involving the preserving of blogs and public tweets. Moreover, we found that the agreement rate for the archiving of government websites already collected under the current legislation was 44%. Ordered logistic analysis clarified that privacy-sensitive respondents tend to resist archival scenarios, and content analysis showed that the disagreement reasons involve concerns over information privacy. Our findings suggest that informed consent and data anonymisation could be effective means to mitigate such concerns.
      Citation: Journal of Information Science
      PubDate: 2022-07-11T12:47:59Z
      DOI: 10.1177/01655515221108692
       
  • SEA-PS: Semantic embedding with attention to measuring patent similarity
           by leveraging various text fields

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      Authors: Zihong Wang, Yufei Liu
      Abstract: Journal of Information Science, Ahead of Print.
      Similarity metrics are critical to identifying the relationships between patents. While many bibliometric methods such as co-citation and co-classification fail to use the vast majority of technical information existing in the text, most text mining methods focus on keywords in only one text field of the patent document. This article aims to leverage various text fields to measure pairwise patent similarity according to their technological bases. A novel approach called semantic embedding with attention for patent similarity (SEA-PS) is proposed. First, the method identifies technological bases and models the semantic relatedness. To achieve this, we put forward an additional patent stop-word list to help extract technical terms with an n-gram-based statistical method. The technical terms are then mapped into a vector space using word embedding. Second, we propose a graph-based method to allocate weights to distinguish the technical focus, considering the linkages between technologies. Finally, we assess the feasibility of the text fields, and integrate their semantics at the patent-level with an attention layer to conduct similarity metrics. The validations are from two perspectives: content validity (coverage of technical information, the validity of semantic representations and effectiveness of text field combinations), and external validity against existing methods via an expert panel. The results demonstrate the superiority of SEA-PS to existing methods, and suggest that ‘abstracts’, ‘claims’ and ‘technical descriptions’ are more effective than ‘titles’. SEA-PS is a fundamental tool for patent retrieval and classification. It also has a broad range of practical applications in innovation and strategy studies, including identifying technological frontiers and studying knowledge spillovers.
      Citation: Journal of Information Science
      PubDate: 2022-07-08T01:02:27Z
      DOI: 10.1177/01655515221106651
       
  • WisRule: First cognitive algorithm of wise association rule mining

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      Authors: Salma Khan, Muhammad Shaheen
      Abstract: Journal of Information Science, Ahead of Print.
      This article proposes a new algorithm for a newly emerging domain wisdom mining that claims to extract wisdom from data. Association rule mining is one of the dominant data mining techniques based on which a new algorithm called WisRule is proposed that generates both positive and negative association rules. These rules can be used for decision-making with less influence from a specialist. The existing algorithms of association rule extraction are based on the frequency of an itemset, which was introduced into the Apriori algorithm for the first time. In these algorithms, those itemsets are converted to the rules of the form Antecedent ⇒ Consequent that qualify the threshold of support, confidence and similar other measures. WisRule is proposed as an extension to the CBPNARM algorithm. WisRule produces both positive and negative association rules based on their frequency evaluated in a certain context (C), utility (U), time (T) and location (L). Rules that are valid in a given context, have high utility and are valid across multiple time intervals and locations become part of the final ruleset. The evaluation of a rule in these four dimensions is claimed as mining wisdom from the given data that is currently used as a hypothetical basis for a domain expert’s decision. WisRule is compared with the Apriori, PNARM and CBPNARM algorithms in terms of precision, recall, number of rules, average confidence, F-measure and execution time.
      Citation: Journal of Information Science
      PubDate: 2022-07-05T11:13:41Z
      DOI: 10.1177/01655515221108695
       
  • Research on the discourse power evaluation of academic journals from the
           perspective of multiple fusion: Taking Medicine, General and Internal
           journals as an example

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      Authors: Xu Wang
      Abstract: Journal of Information Science, Ahead of Print.
      In the open science environment, this article evaluates the discourse power of academic journals from the perspective of multiple integration. It is conducive to scientific research management and provides a reference for enriching and perfecting the evaluation theory and indicators system of academic journals. Based on the theory of evaluation science, discourse power theory and communication theory, first, this article discusses the basic issues of the discourse power evaluation for academic journals. It defines the academic discourse power and the discourse power of academic journals. It is proposed that the discourse power of academic journals is composed of discourse influence and discourse leading. Discourse influence is composed of discourse influence ability and discourse power, and discourse leading is composed of social media discourse, news and policy discourse, encyclopaedia discourse, peer-review discourse and video discourse leading. This article explores the formation process and operation mechanism of the discourse power for academic journals. Then, this article constructs the discourse power evaluation model of academic journals. Second, this article integrates multi-source heterogeneous data, then adopts correlation analysis, integrated factor analysis, entropy weight method, Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method and two-dimensional four-quadrant mapping method to conduct empirical research on the discourse power evaluation of Medicine, General and Internal journals from the perspectives of multi-dimensions, multi-factors, multi-indicators and multi-methods fusion. The results show that the research on the discourse power evaluation for academic journals based on the theory, method and application logic is practical, comprehensive and reliable.
      Citation: Journal of Information Science
      PubDate: 2022-07-05T11:11:59Z
      DOI: 10.1177/01655515221107334
       
  • A comparative analysis of Inventor Patent Classification Coupling between
           the first-inventor and all-inventor: Taking 3D printing as an example

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      Authors: Yanhui Song, Lixin Lei
      Abstract: Journal of Information Science, Ahead of Print.
      This article takes 43,753 patents collected in the Derwent database from 2011 to 2020 as data samples and studies the intellectual structure and evolution of the three-dimensional (3D) printing field in two time periods of 2011–2015 and 2016–2020. We compare the performance of the first-inventor patent classification codes coupling analysis and all-inventor patent classification codes coupling analysis in detecting the intellectual structure of the technical domain. We obtain the following findings: (1) Both methods, the first-inventor patent classification codes coupling analysis and the all-inventor patent classification codes coupling analysis, can show the intellectual structure of the 3D printing field. However, the first-inventor patent classification codes coupling analysis outperforms all-inventor patent classification codes coupling analysis in the detection of basic research; while all-inventor patent classification codes coupling analysis is more sensitive in emerging and interdisciplinary topics. (2) Three-dimensional printing has been evolving in the last decade. While the field has retained the research themes of the previous phase in the last 5 years, several new research themes have also emerged. The most prominent feature of this field is the development and integration of the previous phase’s topics. (3) The research direction of the top 20 inventors in the average coupling frequency ranking is consistent. A small number of researchers continue to work on their previous research, while most inventors move to popular technical topics or combine multiple topics at the same time.
      Citation: Journal of Information Science
      PubDate: 2022-07-05T11:09:00Z
      DOI: 10.1177/01655515221092366
       
  • Improving text relationship modelling with artificial data

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      Authors: Peter Organisciak, Maggie Ryan
      Abstract: Journal of Information Science, Ahead of Print.
      Data augmentation uses artificially created examples to support supervised machine learning, adding robustness to the resulting models and helping to account for limited availability of labelled data. We apply and evaluate a synthetic data approach to relationship classification in digital libraries, generating artificial books with relationships that are common in digital libraries but not easier inferred from existing metadata. Artificial books are generated by remixing existing texts into synthetically constructed formats. We find that for classification on whole–part relationships between books, synthetic data improves a deep neural network classifier by 91%. Furthermore, we consider the ability of synthetic data to learn a useful new text relationship class from fully artificial training data.
      Citation: Journal of Information Science
      PubDate: 2022-07-02T08:45:38Z
      DOI: 10.1177/01655515221093031
       
  • Factors affecting the journal choice for manuscript submission: A
           qualitative study on Turkish medical researchers

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      Authors: Gokhan Tazegul, Erkut Etçioğlu, Emre Emre, Can Özlü
      Abstract: Journal of Information Science, Ahead of Print.
      The factors that affect journal choice for manuscript submission vary, depending on the researcher’s career and field. Although several guidelines are available, information is limited on which criteria are used by Turkish authors. We aimed to evaluate the factors that affect Turkish medical researchers’ journal choice decisions using semi-structured in-depth video conference interviews. The participants identified journal prestige as the major factor, mainly Science Citation Index-Expanded (SCIE) indexing and journal impact factor (JIF), along with acceptance and rejection rates, the age of the journal, and journal audience. Participants identified high publishing costs and mandatory paid open access policy as negative factors. Future policies on covering publishing costs institutionally would be helpful to remove obstacles during journal choice. Turkish medical researchers should be informed on using various indexes and scientometric data to better understand journal performance, rather than only SCIE and JIF.
      Citation: Journal of Information Science
      PubDate: 2022-07-01T11:15:16Z
      DOI: 10.1177/01655515221100724
       
  • Personalised publication recommendation service for open-access digital
           archives

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      Authors: Ahmet Aníl Müngen
      Abstract: Journal of Information Science, Ahead of Print.
      Increase in the number of open-access academic publications and open-access institutional academic archives led more researchers use these archives. No model offers personalised publication suggestions in academic archives. A central service architecture has been proposed towards personalised academic article recommendations for open-access digital archives. Thus, it has been possible to make personalised suggestions for open-access digital archives and enable researchers to discover new publications. A service based on the centralised micro-service architecture model was proposed in the study. Also, TF-IDF and article classification methods were used together for the personalised publication suggestion system. For the first time globally, the proposed method was used with 1464 real users in 49 open-access archives. F-measure success value was found to be higher than 0.8 for recommended publications.
      Citation: Journal of Information Science
      PubDate: 2022-07-01T08:55:09Z
      DOI: 10.1177/01655515221101837
       
  • A new multi-document summarisation approach using saplings growing-up
           optimisation algorithms: Simultaneously optimised coverage and diversity

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      Authors: Cengiz Hark, Taner Uçkan, Ali Karcı
      Abstract: Journal of Information Science, Ahead of Print.
      Automatic text summarisation is obtaining a subset that accurately represents the main text. A quality summary should contain the maximum amount of information while avoiding redundant information. Redundancy is a severe deficiency that causes unnecessary repetition of information within sentences and should not occur in summarisation studies. Although many optimisation-based text summarisation methods have been proposed in recent years, there exists a lack of research on the simultaneous optimisation of scope and redundancy. In this context, this study presents an approach in which maximum coverage and minimum redundancy, which form the two key features of a rich summary, are modelled as optimisation targets. In optimisation-based text summarisation studies, different conflicting objectives are generally weighted or formulated and transformed into single-objective problems. However, this transformation can directly affect the quality of the solution. In this study, the optimisation goals are met simultaneously without transformation or formulation. In addition, the multi-objective saplings growing-up algorithm (MO-SGuA) is implemented and modified for text summarisation. The presented approach, called Pareto optimal, achieves an optimal solution with simultaneous optimisation. Experimentation with the MO-SGuA method was tested using open-access (document understanding conference; DUC) data sets. Performance success of the MO-SGuA approach was calculated using the recall-oriented understudy for gisting evaluation (ROUGE) metrics and then compared with the competitive practices used in the literature. Testing achieved a 26.6% summarisation result for the ROUGE-2 metric and 65.96% for ROUGE-L, which represents an improvement of 11.17% and 20.54%, respectively. The experimental results showed that good-quality summaries were achieved using the proposed approach.
      Citation: Journal of Information Science
      PubDate: 2022-06-29T09:56:50Z
      DOI: 10.1177/01655515221101841
       
  • Publishing in library and information science journals: The success of
           less experienced researchers

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      Authors: Tove Faber Frandsen, Jeppe Nicolaisen
      Abstract: Journal of Information Science, Ahead of Print.
      This study explores the publishing success of less experienced researchers including early career researchers in a selection of library and information science journals. The study includes all authors of articles and reviews published in 10 library and information science journals during a 20-year period (2001–2020). The prior publication of each author is determined at the time of publication in one of the ten journals. The analysis includes 14,612 publications and publication histories of 36,417 authors. The results show that there are considerable differences between journals, and that the share of publications by less experienced researchers has generally decreased over time. Library automation journals publish considerably more publications by early career researchers than information science journals do. Publications in these 10 library and information science journals are being published by authors with an increasing publishing experience and fewer papers are being published by author teams with little experience.
      Citation: Journal of Information Science
      PubDate: 2022-06-29T09:42:07Z
      DOI: 10.1177/01655515221101840
       
  • A novel developmental trajectory discovery approach by integrating main
           path analysis and intermediacy

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      Authors: Shuo Xu, Congcong Wang, Xin An, Liyuan Hao, Guancan Yang
      Abstract: Journal of Information Science, Ahead of Print.
      As a widely used technique for discovering developmental trajectory of a specific field of science and technology, main path analysis armed with global search strategy prefers longer citation paths rather than shorter ones. An obvious feature of longer main paths is that the theme of documents may not be so coherent, though longer paths may provide more details on the development of a field than shorter ones. Thereupon, a new measure, named as intermediacy, was proposed in the literature for recognising important scientific publications. However, the intermediacy is only applicable to the citation network with one single target node and one single source node. For purpose of loosening this limitation of the intermediacy and benefitting from main path analysis and intermediacy, this work raises an alternative approach for discovering developmental trajectory by combining node importance and edge importance via edge and node integrated modes. Extensive experimental results on the weak signals and education fields indicate that similar trajectories can be obtained through these two integrated modes, and richer implications can be encoded in our discovered trajectories than those from main path analysis and intermediacy. In addition, our framework is able to scale very well to a large citation network.
      Citation: Journal of Information Science
      PubDate: 2022-06-29T09:39:40Z
      DOI: 10.1177/01655515221101835
       
  • Data sharing practices across knowledge domains: A dynamic examination of
           data availability statements in PLOS ONE publications

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      Authors: Chenyue Jiao, Kai Li, Zhichao Fang
      Abstract: Journal of Information Science, Ahead of Print.
      As the importance of research data gradually grows in sciences, data sharing has come to be encouraged and even mandated by journals and funders in recent years. Following this trend, the data availability statement has been increasingly embraced by academic communities as a means of sharing research data as part of research articles. This article presents a quantitative study of which mechanisms and repositories are used to share research data in PLOS ONE articles. We offer a dynamic examination of this topic from the disciplinary and temporal perspectives based on all statements in English-language research articles published between 2014 and 2020 in the journal. We find a slow yet steady growth in the use of data repositories to share data over time, as opposed to sharing data in the article and/or supplementary materials; this indicates improved compliance with the journal’s data sharing policies. We also find that multidisciplinary data repositories have been increasingly used over time, whereas some disciplinary repositories show a decreasing trend. Our findings can help academic publishers and funders to improve their data sharing policies and serve as an important baseline dataset for future studies on data sharing activities.
      Citation: Journal of Information Science
      PubDate: 2022-06-29T09:36:04Z
      DOI: 10.1177/01655515221101830
       
  • Classifying the Mexican epidemiological semaphore colour from the Covid-19
           text Spanish news

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      Authors: Miguel A Álvarez-Carmona, Ramón Aranda, Ansel Y Rodríguez-González, Luis Pellegrin, Hugo Carlos
      Abstract: Journal of Information Science, Ahead of Print.
      This work aims to generate classification models that help determine the colour of an epidemiological semaphore (ES) by analysing online news and being better prepared for the different changes in the evolution of the pandemic. To accomplish this, we introduce Cov-NES-Mex corpus, a collection of 77,983 news (labelled with the Mexican ES system) related to Covid-19 for the 32 regions of Mexico. Also, we showed measures that describe the corpus as imbalanced and with a high vocabulary overlap between classes. In addition, evaluation measurements of the pandemic by region are proposed. Furthermore, a classification model, based on a transformer architecture specialised for the Spanish language, achieved up to 0.83 of F-measure. Thus, this work provides evidence that there is essential information in the news that can be used to determine the colour of the ES up to 4 weeks in advance. Finally, the presented results could be applied to other Spanish-speaking countries, which do not have an ES system, thus inferring and comparing their situation concerning the Mexican ES.
      Citation: Journal of Information Science
      PubDate: 2022-06-29T09:33:24Z
      DOI: 10.1177/01655515221100952
       
  • Do retraction practices work effectively' Evidence from citations of
           psychological retracted articles

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      Authors: Siluo Yang, Fan Qi, Heyu Diao, Isola Ajiferukea
      Abstract: Journal of Information Science, Ahead of Print.
      Scientific retraction practices are intended to help purge the continued use of flawed research and assist in maintaining the integrity, credibility and quality of scientific literature. However, the practical effect of retraction is still vague and needs to be further explored. In this study, we analysed the citation counts and sentiments (positive/negative) of retracted articles in psychology journals from Web of Science to explore the effect of retraction. Causal inference strategies were used to measure the net effect of retractions on citation. Results show that the retraction practices induced the citation counts to reduce as expected. However, the proportion of negative citations also decreased because of retraction, indicating an unsatisfied effect. The retraction practice of high-impact factors and open access journals was more effective than other journals. The study integrated an understanding of the dissemination of erroneous publications and provided implications for liabilities involved in the whole retraction process.
      Citation: Journal of Information Science
      PubDate: 2022-06-29T09:29:41Z
      DOI: 10.1177/01655515221097623
       
  • Ensemble correction model for aspect-level sentiment classification

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      Authors: Yiwen Zhou, Lu An, Gang Li, Chuanming Yu
      Abstract: Journal of Information Science, Ahead of Print.
      The aspect-level sentiment analysis is widely used in public opinion analysis. However, the problem of context information loss and distortion with the increase of the model depth is rarely considered in previous research. Few studies have attempted to combine the feature extracted from different embedding models. Based on the correction strategy, the ensemble correction (EC) model proposed in this study can correct context information loss and distortion. Based on the ensemble learning strategy and the weight sharing strategy, EC can extract features from different word embedding models and can reduce computational complexity. Experiments on the resturant14, laptop14, resturant16 and twitter datasets show that the accuracies of the EC model are 0.8848, 0.8213, 0.9301 and 0.7731, respectively. The accuracy of the EC model is higher than state-of-the-art models. Ablation studies and case studies are used to verify the model structure. The optimal number of graph convolutional network (GCN) layers is also verified.
      Citation: Journal of Information Science
      PubDate: 2022-06-29T09:27:01Z
      DOI: 10.1177/01655515221096331
       
  • Hermos: An annotated image dataset for visual detection of grape leaf
           diseases

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      Authors: Tuğba Özacar, Övünç Öztürk, Nurdan Güngör Savaş
      Abstract: Journal of Information Science, Ahead of Print.
      Powdery mildew, dead arm and vineyard downy mildew diseases are frequently seen in the vineyards in the Gediz River Basin, West Anatolia of Turkey. These diseases can be detected early using artificial intelligence (AI)–based systems that can contribute to crop yields and also reduce the labour of the farmer and the amount of pesticides used. This article presents a dataset – namely, Hermos – for use in such AI-based systems. Hermos contains four classes of grape leaf images: leaves with powdery mildew, leaves with dead arm, leaves with downy mildew and healthy leaves. We have currently 492 images and 13,913 labels in the dataset. We have published Hermos in the Linked Open Data (LOD) cloud in order to make it easier for consumers to access, process and manipulate the data.
      Citation: Journal of Information Science
      PubDate: 2022-06-29T09:18:01Z
      DOI: 10.1177/01655515221091892
       
  • Information skills and literacy in investigative journalism in the social
           media era

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      Authors: He Zhang, Haichao Wang
      Abstract: Journal of Information Science, Ahead of Print.
      The study aims to determine the preferred information skills and evaluate information literacy and skills in the social media era on the example of investigative journalism to surmount the majority of challenges it faces. The challenge-based survey conducted among 281 reporting journalists from various countries revealed that their information skills and literacy are average. The survey results show that half of the respondents (52%) recognise the importance of these characteristics, while 38% are sure that information literacy and skills are not necessary and 10% chose the variant ‘I am not sure’. As the indicators show, the main aim for investigative journalists’ writing is to influence the society’s mind about some facts presented in the reports (40%). Only 25% of respondents write with the purpose to present reliable information that indicates the level of their information literacy. The latest strategies in the professional development of investigative journalists in modern social media era allow overcoming the major challenges, including those related to the influence and interests of third parties. The practical significance and prospects of further research are explained by the possibility of using the obtained statistical data to increase the level of information literacy and skills not only of investigative journalists, but also other specialists who work with information.
      Citation: Journal of Information Science
      PubDate: 2022-05-06T03:14:42Z
      DOI: 10.1177/01655515221094442
       
  • Investigation of university websites from technology acceptance model and
           information architecture perspective: A case study

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      Authors: Ferdi Sönmez, Uygar Aydin, Ziya N Perdahci
      Abstract: Journal of Information Science, Ahead of Print.
      Factors, such as whether a website is designed to be user-oriented beyond its mere visual design, its effectiveness and efficiency, its usability, and the organisation of the information it offers, have come to the fore once again after the Covid-19 pandemic. It has been evident that the link structure in a website, better known as the website’s information architecture, helps the practitioners with identifying factors that affect the usability of a website. In this sense, practitioners must ensure that the information architecture supports the usage intentions of a websites’ visitors to better serve and motivate them. However, in many cases, different types of users navigate websites that contain immense amounts of information, so understanding their needs is also important for practitioners. In parallel, this article addresses the problem that different visitors of a large-scale website will need to navigate through dense information to find the information they are looking for, and the information architecture of the website must support different user tasks for the website to be widely adopted. Thus, unlike previous studies, this article combines the principles of information architecture and the technology acceptance model to investigate the effect of information architecture on visitor’s usage intentions. The work also guides practitioners in developing architectural strategies to better enable visitors to fulfil their objectives in the least amount of time.
      Citation: Journal of Information Science
      PubDate: 2022-05-06T03:11:00Z
      DOI: 10.1177/01655515221094436
       
  • Scaling up search engine audits: Practical insights for algorithm auditing

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      Authors: Roberto Ulloa, Mykola Makhortykh, Aleksandra Urman
      Abstract: Journal of Information Science, Ahead of Print.
      Algorithm audits have increased in recent years due to a growing need to independently assess the performance of automatically curated services that process, filter and rank the large and dynamic amount of information available on the Internet. Among several methodologies to perform such audits, virtual agents stand out because they offer the ability to perform systematic experiments, simulating human behaviour without the associated costs of recruiting participants. Motivated by the importance of research transparency and replicability of results, this article focuses on the challenges of such an approach. It provides methodological details, recommendations, lessons learned and limitations based on our experience of setting up experiments for eight search engines (including main, news, image and video sections) with hundreds of virtual agents placed in different regions. We demonstrate the successful performance of our research infrastructure across multiple data collections, with diverse experimental designs, and point to different changes and strategies that improve the quality of the method. We conclude that virtual agents are a promising venue for monitoring the performance of algorithms across long periods of time, and we hope that this article can serve as a basis for further research in this area.
      Citation: Journal of Information Science
      PubDate: 2022-05-02T08:24:02Z
      DOI: 10.1177/01655515221093029
       
  • Representation, detection and usage of the content semantics of comments
           in a social platform

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      Authors: Gianluca Bonifazi, Francesco Cauteruccio, Enrico Corradini, Michele Marchetti, Giorgio Terracina, Domenico Ursino, Luca Virgili
      Abstract: Journal of Information Science, Ahead of Print.
      The analysis of people’s comments in social platforms is a widely investigated topic because comments are the place where people show their spontaneity most clearly. In this article, we present a network-based data structure and a related approach to represent and manage the underlying semantics of a set of comments. Our approach is based on the extraction of text patterns that take into account not only the frequency, but also the utility of the analysed comments. Our data structure and approach are ‘multidimensional’ and ‘holistic’, in the sense that they can simultaneously handle content semantics from multiple perspectives. They are also easily extensible, because additional content semantics perspectives can be easily added to them. Furthermore, our approach is able to evaluate the semantic similarity of two sets of comments. In this article, we also illustrate the results of several tests we conducted on Reddit comments, even if our approach can be applied to any social platform. Finally, we provide an overview of some possible applications of this research.
      Citation: Journal of Information Science
      PubDate: 2022-05-01T02:50:07Z
      DOI: 10.1177/01655515221087663
       
  • Linked data for libraries: Creating a global knowledge space, a systematic
           literature review

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      Authors: Panorea Gaitanou, Ioanna Andreou, Miguel-Angel Sicilia, Emmanouel Garoufallou
      Abstract: Journal of Information Science, Ahead of Print.
      The Semantic Web in general and the Linked Open Data Initiative, in particular, are a growing movement for organisations to make their existing data available in a machine-readable format. Thus, institutions are highly encouraged to publish, share and interlink their data publicly. The more data are opened on the Web (Open Data), the more integrated sets of data will be connected in the Semantic Web (Linked Open Data). Within this context, libraries can complement their data by linking it to other, external data sources. The purpose of this article is to identify papers that refer to linked data in libraries, emphasising the ways that linked data empower libraries to put their knowledge in the context of the open-world, thus enhancing semantic technology innovations. The study considered papers published between 2008 and 2019 in English and presents the collected literature by grouping it according to the topic each paper refers to. The results show that libraries are facing a period of continuing change which present several challenges and indicate that they are moving towards developing new practices, policies and services.
      Citation: Journal of Information Science
      PubDate: 2022-05-01T02:45:50Z
      DOI: 10.1177/01655515221084645
       
  • Quality of reporting of literature search strategies in systematic reviews
           published on the role of telehealth during COVID-19

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      Authors: Fatemeh Sadeghi-Ghyassi, Vahideh Zarea Gavgani, Zahra Fathifar, Nasrin Makani, Reyhaneh Vaez, Maryam Montazeri
      Abstract: Journal of Information Science, Ahead of Print.
      A comprehensive and reproducible search strategy for systematic reviews especially about COVID-19 plays a pivotal role in conducting a reliable and unbiased review. The primary aim of this study was to investigate the quality of the search strategy reporting in systematic reviews conducted on the role of telehealth during COVID-19. The secondary aim of study was to explore some affecting factor in the quality of search strategy. The study evaluated the quality of the search strategy reporting with PRISMA-S checklist. The search was performed in MEDLINE, Embase, CINAHL, and other related databases. Systematic reviews were included. There was no language restriction. The correlation of the PRISMA-S scores with journal impact factor, CiteScore, and librarians’ role were evaluated using Spearman’s rank correlation coefficient. A total of 85 articles were included in the review. The overall mean score of PRISMA-S checklist was 6.12 ± 1.46. PubMed was the most popular database for search. More than half of the studies did not provide a full search strategy. There was a significant positive correlation between PRISMA-S score and the journal impact factor (Spearman’s rho = 0.217; P = 0.46) and CiteScore (Spearman’s rho = 0.235; P = 0.03). The quality of literature search was poor in the included studies. Using the PRISMA-S as a search reporting guideline can be a helpful tool for authors. A professional librarian can be beneficial in improving the quality of the search. It is recommended to use a new pattern in COVID-19-related searches, such as preprint sources.
      Citation: Journal of Information Science
      PubDate: 2022-04-28T04:30:44Z
      DOI: 10.1177/01655515221087649
       
  • Research on information dissemination of blockchain network community
           under the action of negative incentive mechanism

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      Authors: Renqiang Xie, Wende Zhang
      Abstract: Journal of Information Science, Ahead of Print.
      Traditional online communities suffer from false, repetitive or low-level content, with blockchain technology able to solve these problems. Specifically, the incentive mechanism is the blockchain’s core value, including positive and negative incentive mechanisms. The former strengthens people’s behaviour positively, while the latter, on the contrary, adopts mandatory methods such as punishment to eliminate the occurrence of certain types of behaviour. The negative incentive mechanism is the key factor to solve the problems presented above that traditional online communities face. Specifically, this article develops a solution that utilises the negative incentive mechanism, based on the classic infectious disease model (SIR model), introduces smart nodes, puts forward the SSIR model of information dissemination in the blockchain network community, and establishes a set of differential equations reflecting the information dissemination rules. Based on the parameter assumption and solving the equations with MATLAB, this article compares and reveals the changes of different user types on the SIR and SSIR models. Furthermore, we utilise the data collected from the Steemit blockchain community and Sina Weibo platform and apply the Social Network Analysis method to compare and analyse the information dissemination between the blockchain and the traditional network community. The research results highlight that the negative incentive mechanism in the blockchain network community affords a more rational behaviour of user information dissemination, a simpler interaction between users, and reducing to a certain extent the dissemination of ‘distorted’ or ‘uncertain’ information.
      Citation: Journal of Information Science
      PubDate: 2022-04-27T09:15:56Z
      DOI: 10.1177/01655515221087665
       
  • Investigating country-focused studies in Library and Information Science
           journals

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      Authors: Eungi Kim, Eun Sil Kim
      Abstract: Journal of Information Science, Ahead of Print.
      A country-focused study can be defined as a type of study in which the article’s topic is limited to a particular country. Based on the assumption that a country-focused article contains one or more country names in the title or keywords (CNtk), the objective of this study was to investigate the characteristics of country-focused articles in Library and Information Science (LIS) journals. For this study, we selected 30 journals from the Scimago Journal Rank with the highest h-index scores to empirically investigate LIS articles with CNtk. The respective bibliographic records of these journals from 2010 to 2020 were downloaded from Scopus. In terms of subject categories, journals that addressed scientific and technical topics published fewer country-focused articles. In contrast, those on culture, society and government published a higher number of country-focused articles. One exception to this generalisation is that country-focused studies were found to be highly prevalent in certain technical subjects, such as bibliometrics. Although additional empirical evidence is needed for other fields of study, the proposed method seems valuable in analysing journal characteristics since it provides country-specific information about the published articles.
      Citation: Journal of Information Science
      PubDate: 2022-04-23T11:44:38Z
      DOI: 10.1177/01655515221091893
       
  • Axiology of Covid-19 as a linguistic phenomenon

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      Authors: Anastasia Atabekova, Larisa Lutskovskaia, Elena Kalashnikova
      Abstract: Journal of Information Science, Ahead of Print.
      This work’s aim was to investigate what verbal means are used by English-speaking Twitter accounts to describe the pandemic while focusing on extralinguistic factors that are the primary catalysts for linguistic transformations in society. A critical discourse analysis of the lexeme ‘Covid-19’ and words accompanying it was applied. A total of 1736 English-language tweets (6844 lexical units) posted during March to April 2020 were selected for the analysis. Functional discourse analysis allowed systematising and commenting on sampling results as well as provided the opportunity to make the following conclusions. In tweets, the lexeme ‘Covid-19’ is combined not only with the actual name of the virus. This lexeme became a productive ground for derivation into various linguistic structures: substantive word combinations, abbreviations, neologisms and anthropomorphic metaphors. The research results application in international practice will allow linguists to interpret neologisms that emerged as a result of the pandemic and foster the understanding of axiological indicators of native speakers.
      Citation: Journal of Information Science
      PubDate: 2022-04-23T11:40:34Z
      DOI: 10.1177/01655515221091542
       
  • SceneFND: Multimodal fake news detection by modelling scene context
           information

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      Authors: Guobiao Zhang, Anastasia Giachanou, Paolo Rosso
      Abstract: Journal of Information Science, Ahead of Print.
      Fake news is a threat for the society and can create a lot of confusion to people regarding what is true and what not. Fake news usually contain manipulated content, such as text or images that attract the interest of the readers with the aim to convince them on their truthfulness. In this article, we propose SceneFND (Scene Fake News Detection), a system that combines textual, contextual scene and visual representation to address the problem of multimodal fake news detection. The textual representation is based on word embeddings that are passed into a bidirectional long short-term memory network. Both the contextual scene and the visual representations are based on the images contained in the news post. The place, weather and season scenes are extracted from the image. Our statistical analysis on the scenes showed that there are statistically significant differences regarding their frequency in fake and real news. In addition, our experimental results on two real world datasets show that the integration of the contextual scenes is effective for fake news detection. In particular, SceneFND improved the performance of the textual baseline by 3.48% in PolitiFact and by 3.32% in GossipCop datasets. Finally, we show the suitability of the scene information for the task and present some examples to explain its effectiveness in capturing the relevance between images and text.
      Citation: Journal of Information Science
      PubDate: 2022-04-23T11:36:41Z
      DOI: 10.1177/01655515221087683
       
  • The role of psychological, skill level and demographic variables in
           information-seeking behaviours in mental health professionals

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      Authors: Ali Akbari, Mohsen Nowkarizi, Reza Rostami, Ali Moghimi
      Abstract: Journal of Information Science, Ahead of Print.
      The aim of this study was to identify the variables that can potentially affect information-seeking behaviour in mental health service providers using a quasi-experimental research design. The sample included 30 mental health professionals (with minimum 2 years of experience) to each of whom a scenario was presented in which signs and symptoms of three patients were presented, simulating an actual diagnostic interview. Stress response evaluation (SRE), questionnaires, behavioural observation by the Morae software, and semi-structured interviews were used as means of data collection. Our findings showed that variables such as demographic (e.g. field of study, level of education, work experience and age), psychological (e.g. state and trait anxiety, and therapist’s self-assessment) and skill level (e.g. information literacy and expert knowledge) had significant effects on information-seeking behaviour. These results can hopefully provide insights to designers and librarians who seek to create novel or optimise the existing physician-assisted systems.
      Citation: Journal of Information Science
      PubDate: 2022-04-21T09:12:44Z
      DOI: 10.1177/01655515221092363
       
  • Query-focused summarisation in research articles based on semantic
           function of sentences

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      Authors: Yueqian Wang, Yi Bu, Win-bin Huang
      Abstract: Journal of Information Science, Ahead of Print.
      Query-focused summarisation (QFS) in research articles is usually used to help researchers to sum up content related to specific aspects of research articles. However, most QFS approaches failed to consider the inherent structure of research articles to speculate the semantic functions of sentences to make summarisations relate to the given aspect more precisely. Systematic functional linguistic studies suggested that research articles contain inherent structures in which sections and sentences have their specific functions. We suppose these structures can be used as auxiliary information for scientific summarisation. In this article, we seek to improve existing extractive QFS methods by using the macrostructure and discourse segment structure of research articles. We categorise sentences in research articles into different types according to their semantic functions and assign different weights to each type of sentence using an action-based relevance calculation method. We show that our system outperforms the baseline system on a benchmark dataset. Our findings suggest that using the inherent structure of research articles as assistance is practical for scientific summarisation.
      Citation: Journal of Information Science
      PubDate: 2022-04-12T11:28:25Z
      DOI: 10.1177/01655515221086591
       
  • Investigation of information security policy violations among oil and gas
           employees: A security-related stress and avoidance coping perspective

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      Authors: Rao Faizan Ali, PDD Dominic
      Abstract: Journal of Information Science, Ahead of Print.
      Information security is one of the most crucial considerations in digitising Oil and Gas (O&G) organisations. For ensuring information security policy compliance, O&G organisations enforce heavy security requirements. The purpose of this article is to assess how O&G employees cope with stressful information security tasks and how security-related stress (SRS) is related to information security policy violations among O&G employees in developing countries. Based on the coping theory, this article develops a theoretical framework to examine O&G employees’ intention to violate information security policies. The framework is tested using a survey of 270 managers/executives from 150 Malaysian O&G organisations. The results indicated that O&G employees perceive security requirements as stressful to follow and adopt avoidance coping strategies that lead them to violate organisational information security policies. For practitioners, the study findings demonstrate the prevalence of technostress in O&G organisations and suggest alternative mechanisms to address the stressful effects of information security requirements. This article contributes to the information system security literature by testing procrastination and psychological detachment with SRS in the context of developing countries' O&G organisations’ employees and provides an understanding of how O&G employees adopt avoidance coping.
      Citation: Journal of Information Science
      PubDate: 2022-04-07T06:31:09Z
      DOI: 10.1177/01655515221087680
       
  • The digital ecosystem information framework: Insights from action deign
           research

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      Authors: Asif Qumer Gill
      Abstract: Journal of Information Science, Ahead of Print.
      Digital ecosystem (DE) is a dynamic configuration of informational organisms, individual and organisational actors, which interact in the digitally networked and federated environment. Traditional approaches are challenged by the need for handling information in complex DE where information flows beyond the boundary of a single actor. This article presents the informational organism-interaction centric digital ecosystem information (DEi) framework for information operations, management, and governance. The DEi framework emerged based on the insights obtained through the application of well-known thematic network analysis and abstraction, reflection and learning techniques to 15 action design research projects across nine different industry partners in Australia. The DEi framework includes 27 topics that are organised into nine key knowledge and three focus areas. The DEi framework can be used by researchers and practitioners as a resource for designing digital information capabilities as appropriate to their context.
      Citation: Journal of Information Science
      PubDate: 2022-04-05T09:56:56Z
      DOI: 10.1177/01655515221086593
       
  • Questionable conferences and presenters from top-ranked universities

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      Authors: Emanuel Kulczycki, Marek Hołowiecki, Zehra Taşk℩n, Güleda Doğan
      Abstract: Journal of Information Science, Ahead of Print.
      This article aims to investigate the structures of 935 conferences organised by OMICS and 296 conferences organised by WASET from 2015 through 2017. These conferences are characterised in existing literature as so-called predatory or questionable conferences that provide low-quality academic meetings. We analyse 40,224 presenters, focusing on top-ranked institutions according to three global university ranking systems (Academic Ranking of World Universities, Times Higher Education World University Rankings, and QS World University Rankings). Our analysis shows that participants in OMICS events were primarily researchers from the United States, India, the United Kingdom, and China. WASET attracted more researchers from Turkey, India, and South Korea. We found that 11.0% of OMICS and 5.7% of WASET presenters were affiliated with institutions ranked in the top 100 in one of the three aforementioned rankings. We also found that both companies mostly organised conferences in cities that were top tourist destinations.
      Citation: Journal of Information Science
      PubDate: 2022-04-04T10:15:58Z
      DOI: 10.1177/01655515221087674
       
  • Information in the personal collections of writers and artists: Practices,
           challenges and preservation

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      Authors: Maja Krtalić, Jesse David Dinneen
      Abstract: Journal of Information Science, Ahead of Print.
      This article presents findings from interviews with 18 writers and artists in New Zealand, whose lives and work have potential heritage value. The objective was to investigate the perceived value of participants’ personal collections, the relevant management practices and challenges, and their potential effects on preservation and (re)use. The findings provide a characterisation of the personal information management (PIM) practices of writers and artists, revealed challenges common to organising personal collections across time and devices as well as those caused or increased by the nature of writers’ and artists’ work, and produce insights into the impact of perceived collection value and PIM practices on future access, preservation and (re)use of such collections.
      Citation: Journal of Information Science
      PubDate: 2022-04-01T10:02:25Z
      DOI: 10.1177/01655515221084613
       
  • How has academia responded to the urgent needs created by COVID-19' A
           multi-level global, regional and national analysis

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      Authors: Wenjing Zhao, Lin Zhang, Junling Wang, Lili Wang
      Abstract: Journal of Information Science, Ahead of Print.
      In the context of the COVID-19 pandemic, gaining insights into how academia has responded to this urgent challenge is of great significance. This article presents academic response patterns at a global, regional and national level from an analysis of publication volume versus reported cases of COVID-19, scientific collaboration and research focus. We also compare academic activity associated with this newly emerging infection to that related to long-standing infections. Our results show that the research community has responded quickly to COVID-19. The highly developed countries, which have the highest number of confirmed cases, are also the major academic contributors. National-level analysis reveals diverse response patterns from different countries. Specifically, academic research in the United Kingdom remained at a relatively constant level throughout the whole year (2020), while the global share of China’s research output was prone to shift as its domestic pandemic status changed. Strong alliances have formed among countries with academic capabilities in response to the COVID-19 pandemic. The distribution of disciplines is relatively decentralised, indicating that a diverse and broad knowledge base contributes to the COVID-19 literature. Most of the analysed countries show dynamic patterns of research focus that vary over time as the pandemic evolves, except India. As one of the world’s biggest suppliers of vaccines, India makes consistent efforts on vaccine research, especially those related to pharmaceutical preparations. Our findings may serve as resources for fostering strategies to respond to future threats of pandemics.
      Citation: Journal of Information Science
      PubDate: 2022-03-14T06:45:30Z
      DOI: 10.1177/01655515221084646
       
  • Unveiling cognitive structure and comparative advantages of countries in
           knowledge domains

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      Authors: Teresa Muñoz-Écija, Benjamín Vargas-Quesada, Zaida Chinchilla-Rodríguez
      Abstract: Journal of Information Science, Ahead of Print.
      Mapping and depicting the structure, dynamics and national specialisation profiles of scientific fields at the country level affords a better understanding of national developments and changes in a given field, particularly when these changes may serve as an aid in decision-making with regard to research management. This article looks at the cognitive structure of a field over time to characterise its development across countries and to appraise the competitiveness of countries in terms of research specialisation. Based on a dataset extracted from the Scopus database, we conducted a co-word analysis and studied the degree of specialisation based on publications and on keywords, in the Nanoscience and Nanotechnology field (NST). The results reveal that NST research tends to focus on nano applications and devices. According to the keyword activity index, the countries studied centre their specialisation on electronic, biotechnology and biomedical research, certain countries showing a more competitive edge in the global realm of output. Accordingly, implications that could contribute to decision-making regarding the economy and research policies are described.
      Citation: Journal of Information Science
      PubDate: 2022-03-14T06:42:30Z
      DOI: 10.1177/01655515221084607
       
  • Research knowledge utilisation for societal impact: Information practices
           based on abductive topic modelling

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      Authors: Han Zheng, LG Pee
      Abstract: Journal of Information Science, Ahead of Print.
      Information science researchers are increasingly seeking to understand the utilisation of knowledge generated through scientific research outside of academia. Although the conceptual levels of knowledge utilisation are well established, our understanding of the various information practices for knowledge utilisation employed by researchers remains limited. This study identified such information practices by text-mining 6637 case studies documented under the United Kingdom’s Research Excellence Framework. The results were augmented with expert judgement to develop a framework consisting of nine types based on the theoretical framework of research knowledge utilisation. Three emerging types were identified: deliberation, co-creation and foresighting. They indicate the rise of information practices leveraging social media and analytical capabilities to engage potential beneficiaries in using and realising the value of research.
      Citation: Journal of Information Science
      PubDate: 2022-03-07T11:58:06Z
      DOI: 10.1177/01655515221081354
       
  • A web-based information intervention for family caregivers of patients
           with Dementia: A randomized controlled trial

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      Authors: Simin Salehinejad, Nazanin Jannati, Mohammad Azami, Moghaddameh Mirzaee, Kambiz Bahaadinbeigy
      Abstract: Journal of Information Science, Ahead of Print.
      This study aimed to evaluate the efficacy of a web-based health information intervention on knowledge, care burden and attitudes of family caregivers of patients with dementia. This study is a unblinded randomised controlled trial. The study population consisted of family caregivers of patients with dementia (n = 50) which were randomly allocated to the intervention group (access to the web-based health information) or control group (access to information as usual). The participants completed knowledge, care burden and attitude questionnaire at baseline and at two months follow-up. A total of 50 caregivers participated in this study. Before the intervention, there was no statistically significant difference between the knowledge, care burden and attitude score between the two groups (p> 0.001). In comparison to the control group after the intervention, participants in the intervention group showed significant improvements in all outcomes (p < 0.001). These findings provide further evidence that web-based information interventions helped caregivers feel more confident, empathetic and concerned about dementia care with less care burden.
      Citation: Journal of Information Science
      PubDate: 2022-03-07T11:55:41Z
      DOI: 10.1177/01655515221081353
       
  • Journal of Information Science: A gender-based bibliometricstudy
           (2015–2020)

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      Authors: Silvia Cobo-Serrano, Rosario Arquero-Avilés, Gonzalo Marco-Cuenca
      Abstract: Journal of Information Science, Ahead of Print.
      Scientific publication is one of the main channels for disseminating research results and one of the most important means of determining the presence of women in scientific research. This article aims to conduct a bibliometric analysis in the Library and Information Science (LIS) field, from a gender perspective, by analysing Journal of Information Science (2015–2020). To reach this goal, the research has been developed in several stages (data collection, gender authors’ identification, validation of authorities, contact by email and analysis of results) to identify 326 contributions and 697 authors finally. Analysis patterns showed outcomes on gender (single and multiple authors), scientific collaboration, authorship time-course, authorship productivity as well as institutional and geographic affiliation. Some conclusions show that male and female authors are not equally represented in the journal, with a great difference in the case of collective authorship. Overall, there is a clear trend of single and multiple male authorship.
      Citation: Journal of Information Science
      PubDate: 2022-03-05T09:38:15Z
      DOI: 10.1177/01655515221081346
       
  • MathUSE: Mathematical information retrieval system using universal
           sentence encoder model

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      Authors: Pankaj Dadure, Partha Pakray, Sivaji Bandyopadhyay
      Abstract: Journal of Information Science, Ahead of Print.
      In the scientific field, mathematical formulae are a significant factor in communicating the ideas and the fundamental principles of any scientific knowledge. Nowadays, the scientific research community generates a huge number of documents that comprise both textual and mathematical formulae. For the retrieval of textual information, numerous retrieval systems are present that generate excellent results. Nevertheless, these textual information retrieval systems are insufficient to handle the structure and scripting styles of the mathematical formulae. The recent past has perceived the research, which intends to retrieve the textual and mathematical formulae, but their impoverished results are symptomatic to the scope of improvement. In this article, we have implemented the formula-embedding approach, which encodes the formulae into fixed dimensional embedding vectors. For encoding of formula, we have used universal sentence encoder–based sentence-embedding model, which relies on transformer architecture and deep averaging network. The proposed models take the latex formula as an input and produce an output of fixed dimensional embedding representation. To achieve more promising results, the transformer model follows stacked self-attentions, point-wise fully connected layers and positional encoding for both the encoder and decoder. The obtained results have been compared with state-of-the-art existing approaches, and the comparison study revealed that the proposed approach offers better retrieval accuracy in terms of [math] = 0.217, [math] = 0.178 and P@10 = 0.378 measures.
      Citation: Journal of Information Science
      PubDate: 2022-03-04T12:31:19Z
      DOI: 10.1177/01655515221077335
       
  • Enterprise resource planning adoption model for well-informed decision in
           higher learning institutions

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      Authors: Muaadh Mukred, Fahad M Alotaibi, Zawiyah M Yusof, Umi Asma’ Mokhtar, Burkan Hawash, Waleed Abdulkafi Ahmed
      Abstract: Journal of Information Science, Ahead of Print.
      Enterprise resource planning (ERP) has been found to have a key role in the management of higher learning institutions (HLIs) and schools. However, the literature shows no universal model to support and shed light on the adoption of ERP, which lessens the chances for an effective ERP adoption and usage. Therefore, a new model is needed for successful adoption and the eventual enhanced decision-making, and as such, there is a need to investigate the factors that can bring about ERP system adoption. Models for ERP adoption in literature are few and far between, and what few exist are not applicable as they do not cover all the major factors that can contribute to adoption success. Hence, in this article, an ERP adoption model was brought forward for HLIs for the promotion of their decision-making process. The model was developed through the integration of DeLone and McLean’s information success model and the technology, organisation and environment (TOE) theory. The study distributed 500 survey questionnaire copies online and collected 364 from HLIs respondents, after which they were retrieved, and data were analysed through partial least squares structural equation modeling (PLS-SEM) 3 statistical software. On the basis of the obtained analysis findings, technological, organisational and environmental factors had significant and positive effects on ERP adoption, and ERP adoption had a positive and significant effect on the decision-making of HLIs. The entire factors were found to be significant in their effects, and ERP adoption sufficiently explained variance extracted from decision-making. The study contributes to the literature through the pioneering measurement of factors categorised under technological, organisational and environmental dimensions, with ERP adoption and decision-making encapsulated in a single model.
      Citation: Journal of Information Science
      PubDate: 2022-03-04T04:15:16Z
      DOI: 10.1177/01655515211019703
       
  • Detecting agro: Korean trolling and clickbaiting behaviour in online
           environments

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      Authors: Eun Been Choi, Jisu Kim, Dahye Jeong, Eunil Park, Angel P del Pobil
      Abstract: Journal of Information Science, Ahead of Print.
      This article presents one of the first approaches to provide the understanding of agro (one of the unique eye-attracting cues) headlines and thumbnails in online video sharing platform, YouTube. We annotated 1881 headlines and thumbnails, based on agro and the type of agro. Then, we experimented with machine learning models to classify agro data from the non-agro data. With a bidirectional long short-term memory (Bi-LSTM) model, we achieved 84.35% of accuracy in detecting agro headlines and 82.80% of accuracy in detecting agro thumbnails. We believe that the automatic detection of agro headlines can allow users to have better experience in browsing through and getting the content that they want online.
      Citation: Journal of Information Science
      PubDate: 2022-02-25T12:09:39Z
      DOI: 10.1177/01655515221074325
       
  • Collaboration of issuing agencies and topic evolution of health
           informatisation policies in China

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      Authors: Wenli Zhang, Rui Yao, Richard Evans, Wenjing Huang, Guang Cao, Lining Shen
      Abstract: Journal of Information Science, Ahead of Print.
      Digital transformation in the Chinese healthcare industry has led national government agencies to issue a series of policies to guide the construction of health informatisation. However, little is known about the issuing agencies and the topics of health informatisation policies. This study aimed to explore the collaboration of policies issuing and the evolution of policy topics. In this study, a total of 156 policy documents were identified. Author–Topic model and pre-discretised method based on Latent Dirichlet Allocation model were employed to mine the correlation between the issuing agencies and policy topics, and the evolution of policy contents. Findings suggest that the development of health informatisation policies can be divided into three stages. The number of policies has been increasing constantly, among which the policy of opinion and notification accounts for the vast majority. Many government agencies are involved in formulating policies collaboratively. On the whole, the topics changed constantly over time. From 2003 to 2008, policy topics focused on standards and specifications, with the phenomenon of splitting and development. From 2009 to 2014, policies were predominantly related to the construction of regional health informatisation, with some emerging topics generating. Internet + medical and new information technology gained attention from 2015 to 2020; most topics in this period were inherited, split or merged from the previous period. This study is helpful to research and formulation of the health informatisation-related policies.
      Citation: Journal of Information Science
      PubDate: 2022-02-24T09:40:33Z
      DOI: 10.1177/01655515221074323
       
  • The impact of social noise on social media and the original intended
           message: BLM as a case study

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      Authors: Nayana Pampapura Madali, Manar Alsaid, Suliman Hawamdeh
      Abstract: Journal of Information Science, Ahead of Print.
      Social media has become a platform for information diffusion, voicing concerns of existing inequalities and raising public awareness of various social and societal issues. Despite the social good, social media has become a fertile ground for spreading misinformation, hate speech and conspiracy theories. The death of George Floyd in May 2020 triggered a series of protests worldwide in support of the Black Lives Matter (BLM) movement and triggered a debate about equity, inclusion and social justice. The purpose of this study is to examine the impact of misinformation and social noise on the original intended message of BLM using data from the Twitter hashtag ‘BLM’. Results from topic modelling have shown the strong presence of misinformation and social noise. Such information was most probably intended to influence, mislead and dilute the original intended message. However, despite the effort to distort the original message of BLM, results from sentiment analysis show that users’ opinions of the BLM movement remained positive.
      Citation: Journal of Information Science
      PubDate: 2022-02-23T11:41:53Z
      DOI: 10.1177/01655515221077347
       
  • Exploring the topic evolution of Dunhuang murals through image
           classification

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      Authors: Ziming Zeng, Shouqiang Sun, Tingting Li, Jie Yin, Yueyan Shen, Qian Huang
      Abstract: Journal of Information Science, Ahead of Print.
      Dunhuang is a unique art treasure and a world heritage site. In order to organise and manage Dunhuang cultural heritage resources, this article studies the classification of Dunhuang murals in different dynasties, and explores the topic distribution characteristics and evolution rules of them. First, image features are extracted through scale-invariant feature transform (SIFT) and Canny and scale-invariant feature transform (CSIFT), a visual dictionary is generated through the k-means clustering algorithm, and the term frequency–inverse document frequency (TF-IDF) vector is calculated and combined with the colour feature vector extracted via hue, saturation and value (HSV). Second, Dunhuang mural images are collected and the support vector machine (SVM) classifier is built. Finally, the knowledge graph-based topic maps are constructed, and graph theory is introduced to analyse the topic distribution and evolution of Dunhuang murals in different dynasties. The results show that the Dunhuang murals of different dynasties can be effectively classified through the bag of words, HSV and support vector machine (BOW_HSV_SVM) based on their visual features. Through topic maps, the topic distribution characteristics and evolution rules of Dunhuang murals with the dynasties are revealed.
      Citation: Journal of Information Science
      PubDate: 2022-02-23T11:40:12Z
      DOI: 10.1177/01655515221074336
       
  • Gender differences in citation sentiment: A case study in life sciences
           and biomedicine

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      Authors: Tahereh Dehdarirad, Maryam Yaghtin
      Abstract: Journal of Information Science, Ahead of Print.
      In this study, we investigated whether female and male authors in the field of life sciences and biomedicine differed in their tendency for citation and citation sentiment. The data comprised two sets, cited set and citing set. Cited set comprised 17,237 articles whereas citing set comprised 115,935 articles. The cited set which is from the area Life Sciences & Biomedicine and published during 2012–2016 was retrieved from the Web of Science Medline. The citing set and its citation contexts were retrieved using the Colil database. The analysis was done using a combination of homophily analysis, regression analysis and a chi-square test. The covariates in the regression analyses were features related to authors, journal, institution, country and abstract readability. The homophily analysis showed a significant tendency for female (8%) and male (14%) authorship teams to cite papers by the same gender composition teams. In addition, the results of regression analysis (Model 1) and pairwise comparisons showed that male-authored papers received a significant higher positive sentiment compared with female-authored papers. The results of regression analysis (Model 2) showed a small significant positive association between gender similarity of cited and citing authorship teams and the sentiment score. However, further analysis using the chi-square test showed a significant lower tendency for women to use positive terms when citing the research findings of papers with the same gender composition. Men, in contrast, used significantly more positive terms when citing papers with the same gender composition. Finally, lay summary for a cited paper, country similarity and the venue of cited publication when it was a mega journal had a positive significant association with the sentiment score received.
      Citation: Journal of Information Science
      PubDate: 2022-02-23T11:27:05Z
      DOI: 10.1177/01655515221074327
       
  • Information literacy and research support services in academic libraries:
           A bibliometric analysis from 2001 to 2020

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      Authors: Nusrat Ali, Muhammad Shoaib, Farooq Abdullah
      Abstract: Journal of Information Science, Ahead of Print.
      This article attempted to examine research support services, information services, print collections, digital resources and information literacy using bibliometric analysis from 2001 to 2020. The main aim was to consolidate the published studies on the research support services in academic libraries in the Web of Science (WoS) indexed documents. However, there has been a lack of quantitative measurements on the subject. Thus, we used the bibliometric method and found a total of 4079 published documents. The study findings revealed that the topic of ‘information literacy and library’ was on top with a total number of 2168 publications, 3047 articles as a type of published documents, 3662 publications in English and a considerable increase in publications as per years were found. The top author named Fourie I was found with 106 citations and 22 articles started in 2001. Similarly, the University of Illinois found on top organisations out of 2609, United States on top out of 113 countries and information literacy as a keyword out of 6179. Furthermore, the Journal of Academic Librarianship placed at top of sources out of 726 and the National Institutes of Health NIH USA as a top funding agency.
      Citation: Journal of Information Science
      PubDate: 2022-02-12T06:18:45Z
      DOI: 10.1177/01655515211068169
       
  • COVID-19 effect on the gender gap in academic publishing

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      Authors: Dariusz Jemielniak, Agnieszka Sławska, Maciej Wilamowski
      Abstract: Journal of Information Science, Ahead of Print.
      The authors wanted to verify a popular belief that women scholars have been disproportionately affected by the COVID-19 pandemic. We studied the first names of authors of 266,409 articles from 2813 journals in 21 disciplines, and we found no significant differences between men and women in publication patterns between 2021, 2020, and 2019 overall. However, we found significant differences in publication patterns between gender in different disciplines. In addition, in disciplines where the proportion of women authors is higher, there are fewer single-authored articles. In the multi-author articles if the first author is female, there is more gender balance among authors, although there are still fewer women co-authors.
      Citation: Journal of Information Science
      PubDate: 2022-02-12T06:16:45Z
      DOI: 10.1177/01655515211068168
       
  • TRSAv1: A new benchmark dataset for classifying user reviews on Turkish
           e-commerce websites

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      Authors: Murat Aydoğan, Veysel Kocaman
      Abstract: Journal of Information Science, Ahead of Print.
      The amount of data produced significantly increased with the development of Internet technologies. Accordingly, the importance of natural language processing studies increased, and this topic became one of the most studied artificial intelligence subjects. Even though it is a popular topic that is widely studied on, not enough studies have been conducted on the Turkish language. Even the studies conducted in Turkey are primarily on English and other natural languages instead of Turkish. The lack of a Turkish dataset is the most crucial reason for the lack of studies. Therefore, to create a solution, user reviews on e-commerce websites were collected and labelled reviews as positive, negative and neutral, and a new and unique dataset consisting of 150,000 reviews was created. This dataset was named TRSAv1, which was publicly shared with the researchers will contribute to the Turkish natural language processing studies; however, the effect of different word representation methods on algorithm performance was examined in detail, and the results were compared.
      Citation: Journal of Information Science
      PubDate: 2022-02-10T08:30:53Z
      DOI: 10.1177/01655515221074328
       
  • Agent-based model: A method worthy of promotion in Library and Information
           Science

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      Authors: Peng Su, Meihua Chen, Yanfei Wang
      Abstract: Journal of Information Science, Ahead of Print.
      Agent-based model (ABM) is a branch of artificial intelligence. Its specialty is to construct a complex macro-system model by describing the perception, decision, learning and action of micro-agents. This method is widely used in many fields from natural science to social science. We discuss ABM by collecting relevant academic papers which apply to the field of Library and Information Science (LIS). This article systematically reviews how ABM is applied to the LIS field and argues that ABM can provide an exploratory tool with quantifiability, repeatability, interpretability, contingency, adaptability and other types of advantages. Finally, it is pointed out that this method is a research tool worthy of careful exploration.
      Citation: Journal of Information Science
      PubDate: 2022-02-04T08:55:27Z
      DOI: 10.1177/01655515211061867
       
  • Predictors affecting personal digital information management activities: A
           hierarchical regression analysis

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      Authors: Alia Arshad, Qurat Ul Ain Saleem, Khalid Mahmood
      Abstract: Journal of Information Science, Ahead of Print.
      The study aimed to investigate the influence of demographic characteristics, Internet use, computer knowledge and technology self-efficacy on personal digital information management (PIM) activities – information finding, information keeping, information organising and information re-finding. The design of the study was quantitative and a survey method was used to get the objectives of the study. Three independent institutes of art and design – the Institute of Art and Culture, the National College of Arts and Design and the Pakistan Institute of Fashion and Design – were chosen as the research setting of the study. The population of the study was an academic community of three art and design institutes. The questionnaire was distributed to faculty and students of respective institutes and 229 responses were received after follow-up. The findings of the study indicated that both demographic characteristics and technology-related factors influenced the arts and design academic community’s PIM activities. However, the second set of variables – Internet use, computer knowledge and technology self-efficacy influenced more than demographic variables on PIM activities. Academic role, university and technology self-efficacy appeared significant predictors of all PIM activities. The findings might be helpful for arts and design institutes librarians to make strategies to improve academic community’s personal information management skills. In arts and design institutes, better efficiency in faculty and students’ PIM could be achieved if PIM literacy programmes are designed paying attention to differentials in demographic factors and technology-related factors, as revealed in this study.
      Citation: Journal of Information Science
      PubDate: 2022-02-03T12:14:37Z
      DOI: 10.1177/01655515211072299
       
  • E-reading consumption among Pakistani digital immigrants: A mixed-methods
           approach

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      Authors: Saira Hanif Soroya, Mohsin Abdur Rehman, Iqra Tariq, Almas Khanum, Faiza Bashir
      Abstract: Journal of Information Science, Ahead of Print.
      Digital information adoption among the older generation is becoming interesting, and e-reading consumption is an important phenomenon. The current study explores the e-reading consumption experience among Pakistani generation X readers (Xers) through the theory of planned behaviour (TPB), along with TPB model validation through a larger sample. A mixed-method research design (exploratory sequential) was employed. The study was completed in two phases; the first phase was qualitative based on nine (n = 9) in-depth face-to-face interviews. In the second phase, a quantitative research design was employed. A survey questionnaire was developed based on the TPB model and outcomes of the first phase, and the data were collected from 250 Xers from Pakistani public libraries. The first phase outlined numerous positive consequences and challenges specific to the behavioural beliefs. The circle of friends, colleagues and supervisors encourage e-reading consumption given the benefits, speed, and saver of time, cost, and effort, to name a few. Notably, e-reading consumption intention leaves no alternative for Xers in the digital information era. The results of second phase show that seven out of nine proposed hypotheses were supported significantly H2 (β = 0.33, p = .00), H3 (β = 0.20, p = .02), H4 (β = 0.27, p = .00), H6 (β = 0.22, p = .01), H7 (β = 0.18, p = .03), H8 (β = .28, p = .00) and H9 (β = −0.15, p = .04), whereas H1 (β = −0.03, p = .66) and H5 (β = −0.02, p = .73) were rejected. The current study extends the theoretical foundations of TPB in the age of digital information consumption by exploring dimensions qualitatively and tested that proposed relationship quantitatively from a developing country context, Pakistani Xers.
      Citation: Journal of Information Science
      PubDate: 2022-02-03T12:05:47Z
      DOI: 10.1177/01655515211061868
       
  • Expert-recommended biomedical journal articles: Their retractions or
           corrections, and post-retraction citing

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      Authors: Peiling Wang, Jing Su
      Abstract: Journal of Information Science, Ahead of Print.
      Faculty Opinions has provided recommendations of important biomedical publications by domain experts (FMs) since 2001. The purpose of this study is two-fold: (1) identify the characteristics of the expert-recommended articles that were subsequently retracted and (2) investigate what happened after retraction. We examined a set of 232 recommended, later retracted or corrected articles. These articles were classified as New Finding (43%), Interesting Hypothesis (16%), and so on. More than 71% of the articles acknowledged funding support; the National Institutes of Health, USA (NIH) was a top funder (64%). The top reasons for retractions were Errors of various types (28%); Falsification/fabrication of data, image, or results (20%); Unreliable data, image, or results (16%); and Results not reproducible (16%). Retractions took from less than 2 months to more than 15 years. Only 15% of recommendations were withdrawn either after dissents were made by other FMs or after retractions. Most of the retracted articles continue to be cited post-retraction, especially those published in Nature, Science, and Cell. Significant positive correlations were observed between post-retraction citations and pre-retraction citations, between post-retraction citations and peak citations, and between post-retraction citations and the post-retraction citing span. A significant negative correlation was also observed between the post-retraction citing span and years taken to reach peak citations. Literature recommendation systems need to update the changing status of the recommended articles in a timely manner; invite the recommending experts to update their recommendations; and provide a personalised mechanism to alert users who have accessed the recommended articles on their subsequent retractions, concerns, or corrections.
      Citation: Journal of Information Science
      PubDate: 2022-02-02T10:07:46Z
      DOI: 10.1177/01655515221074329
       
  • #fridaysforfuture – What does Instagram tell us about a social
           movement'

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      Authors: Christoph Herrmann, Sebastian Rhein, Isabelle Dorsch
      Abstract: Journal of Information Science, Ahead of Print.
      Understanding social movement structures is important for political decision-makers to enable them to recognise the various motivating factors behind these movements. The Fridays for Future movement characterises a political group that has a majority of young people, frequently using social media to organise actions. By conducting a social network analysis on hashtags, this study contributes to the understanding of the global Fridays for Future movement. Particularly, we focus on the use and connection of hashtags on Instagram. We collected 59,112 posts tagged with #fridaysforfuture and analysed 91,172 hashtags used therein. Subsequently, the 140 most used hashtags were divided into 11 clusters, which provide not only information about the organisation of the social movement via social media, but also insights into lifestyle-related aspects. The clusters include the topics: climate; nutrition, lifestyle and health; memes; cycling; art; sustainable consumption and the Earth Day. The article shows that the motives of the Fridays for Future movement are broad. We can demonstrate that Fridays for Future is connected to other social movements and gain insights into the everyday life of the Fridays for Future stakeholders.
      Citation: Journal of Information Science
      PubDate: 2022-02-01T09:01:52Z
      DOI: 10.1177/01655515211063620
       
  • Ranking of Iranian medical universities based on altmetric indices

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      Authors: Aboozar Ramezani, Seyed Javad Ghazimirsaeed, Fatemeh Ramezani-Pakpour-Langroudi, Hasan Siamian, Mohammad Hossein YektaKooshali, Ahmad Papi, Kobra Aligolbandi
      Abstract: Journal of Information Science, Ahead of Print.
      This study was aimed at evaluating the Iranian medical universities’ rankings and altmetric indices in ResearchGate and Academia.edu. This cross-sectional analytical study was conducted using a scientometric method. Social networking measures were collected in MS Excel from January to February 2017. Data were analysed using SPSS software and the Spearman, chi-square and Kendall rank coefficient tests. Ranking information and altmetric indices of 50 Iranian medical universities were collected and analysed. All of the type-1 medical universities have been presented in the Academia.edu and ResearchGate social networks. A statistically significant relationship (P 
      Citation: Journal of Information Science
      PubDate: 2022-01-20T05:11:51Z
      DOI: 10.1177/01655515211072300
       
  • KNNHI: Resilient KNN algorithm for heterogeneous incomplete data
           classification and K identification using rough set theory

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      Authors: Ahmed Hamed, Mohamed Tahoun, Hamed Nassar
      Abstract: Journal of Information Science, Ahead of Print.
      The original K-nearest neighbour (KNN) algorithm was meant to classify homogeneous complete data, that is, data with only numerical features whose values exist completely. Thus, it faces problems when used with heterogeneous incomplete (HI) data, which has also categorical features and is plagued with missing values. Many solutions have been proposed over the years but most have pitfalls. For example, some solve heterogeneity by converting categorical features into numerical ones, inflicting structural damage. Others solve incompleteness by imputation or elimination, causing semantic disturbance. Almost all use the same K for all query objects, leading to misclassification. In the present work, we introduce KNNHI, a KNN-based algorithm for HI data classification that avoids all these pitfalls. Leveraging rough set theory, KNNHI preserves both categorical and numerical features, leaves missing values untouched and uses a different K for each query. The end result is an accurate classifier, as demonstrated by extensive experimentation on nine datasets mostly from the University of California Irvine repository, using a 10-fold cross-validation technique. We show that KNNHI outperforms six recently published KNN-based algorithms, in terms of precision, recall, accuracy and F-Score. In addition to its function as a mighty classifier, KNNHI can also serve as a K calculator, helping KNN-based algorithms that use a single K value for all queries that find the best such value. Sure enough, we show how four such algorithms improve their performance using the K obtained by KNNHI. Finally, KNNHI exhibits impressive resilience to the degree of incompleteness, degree of heterogeneity and the metric used to measure distance.
      Citation: Journal of Information Science
      PubDate: 2022-01-11T09:48:26Z
      DOI: 10.1177/01655515211069539
       
  • Essential elements, conceptual foundations and workflow design in
           crowd-powered projects

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      Authors: Celso A S Santos, Alessandro M Baldi, Fábio R de Assis Neto, Monalessa P Barcellos
      Abstract: Journal of Information Science, Ahead of Print.
      Crowdsourcing arose as a problem-solving strategy that uses a large number of workers to achieve tasks and solve specific problems. Although there are many studies that explore crowdsourcing platforms and systems, little attention has been paid to define what a crowd-powered project is. To address this issue, this article introduces a general-purpose conceptual model that represents the essential elements involved in this kind of project and how they relate to each other. We consider that the workflow in crowdsourcing projects is context-oriented and should represent the planning and coordination by the crowdsourcer in the project, instead of only facilitating decomposing a complex task into subtask sets. Since structural models are limited to cannot properly represent the execution flow, we also introduce the use of behavioural conceptual models, specifically Unified Modeling Language (UML) activity diagrams, to represent the user, tasks, assets, control activities and products involved in a specific project.
      Citation: Journal of Information Science
      PubDate: 2022-01-11T09:45:11Z
      DOI: 10.1177/01655515211062466
       
  • Knowledge management activities: Conceptual foundations and research
           issues

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      Authors: William B Edgar, Kendra S Albright
      Abstract: Journal of Information Science, Ahead of Print.
      Knowledge is a broad concept whose epistemological construct has been debated since the days of the early Greek philosophers. Knowledge was discussed extensively during the Renaissance, became a central area of study during the Scientific Revolution and was applied extensively within organisations throughout the Industrial Revolution. Knowledge became an organisational resource of significant interest, emerging over the past 25 years as a unique field of study called knowledge management (KM). Much of the KM literature addresses matters of practice and application; what is missing is a deep and conceptual analysis of the activities that drive KM processes. This article provides a conceptualisation of KM activities focusing on the underlying foundations of these activities. The result is a rich framework of KM activities that can be used to pursue important research areas involved in studying KM processes, including theory development, areas of overlap and where further research is needed.
      Citation: Journal of Information Science
      PubDate: 2022-01-08T08:52:28Z
      DOI: 10.1177/01655515211069538
       
  • The relationship between sentiment score and COVID-19 cases in the United
           States

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      Authors: Truong (Jack) P Luu, Rosangela Follmann
      Abstract: Journal of Information Science, Ahead of Print.
      The coronavirus disease (COVID-19) continues to have devastating effects across the globe. No nation has been free from the uncertainty brought by this pandemic. The health, social and economic tolls associated with it are causing strong emotions and spreading fear in people of all ages, genders and races. Since the beginning of the COVID-19 pandemic, many have expressed their feelings and opinions related to a wide range of aspects of their lives via Twitter. In this study, we consider a framework for extracting sentiment scores and opinions from COVID-19–related tweets. We connect users’ sentiment with COVID-19 cases across the United States and investigate the effect of specific COVID-19 milestones on public sentiment. The results of this work may help with the development of pandemic-related legislation, serve as a guide for scientific work, as well as inform and educate the public on core issues related to the pandemic.
      Citation: Journal of Information Science
      PubDate: 2022-01-08T08:51:10Z
      DOI: 10.1177/01655515211068167
       
 
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