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Journal of Information Science
Journal Prestige (SJR): 0.674
Citation Impact (citeScore): 2
Number of Followers: 1234  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0165-5515 - ISSN (Online) 1741-6485
Published by Sage Publications Homepage  [1090 journals]
  • A content-based technique for linking dual language news articles in an
    • Authors: Muzammil Khan, Arif Ur Rahman, Arshad Ahmad, Sarwar Shah Khan
      Abstract: Journal of Information Science, Ahead of Print.
      To retrieve a specific news article from a vast archive containing multilingual news articles against a user query or based on similarity among news articles is a challenging task. The task becomes even further complicated when the archive contains articles from a low resourced and morphologically complex language like Urdu, along with English new articles. The article proposes a content-based (lexical) similarity measure, that is, Common Ratio Measure for Dual Language (CRMDL), for linking digital news articles published in various online news sources. The similarity measure links Urdu-to-English news articles during the preservation process using an Urdu-to-English lexicon. A literature review showed that an Urdu-to-English lexicon did not exist, and therefore, the first task was to build a lexicon from multiple sources. The proposed similarity measure, that is, CRMDL, is evaluated rigorously on different data sets, of varying sizes, to assess the effectiveness. The experimental results show that the proposed measure is feasible and effective for similarity computation between Urdu and English news articles, which can obtain, on average, 50% precision and 67% recall. The performance can be improved sufficiently by managing the limitations summarised in the study.
      Citation: Journal of Information Science
      PubDate: 2020-08-04T02:29:31Z
      DOI: 10.1177/0165551520937614
  • REDI: Towards knowledge graph-powered scholarly information management and
           research networking
    • Authors: José Ortiz Vivar, José Segarra, Boris Villazón-Terrazas, Víctor Saquicela
      Abstract: Journal of Information Science, Ahead of Print.
      Academic data management has become an increasingly challenging task as research evolves over time. Essential tasks such as information retrieval and research networking have turned into extremely difficult operations due to an ever-growing number of researchers and scientific articles. Numerous initiatives have emerged in the IT environments to address this issue, especially focused on web technologies. Although those approaches have individually provided solutions for diverse problems, they still can not offer integrated knowledge bases nor flexibility to exploit adequately this information. In this article, we present REDI, a Linked Data-powered framework for academic knowledge management and research networking, which introduces a new perspective of integration. REDI combines information from multiple sources into a consolidated knowledge base through state-of-the-art procedures and leverages semantic web standards to represent the information. Moreover, REDI takes advantage of such knowledge for data visualisation and analysis, which ultimately improves and simplifies many activities including research networking.
      Citation: Journal of Information Science
      PubDate: 2020-08-04T02:28:16Z
      DOI: 10.1177/0165551520944351
  • Evaluating the quality of linked open data in digital libraries
    • Authors: Gustavo Candela, Pilar Escobar, Rafael C Carrasco, Manuel Marco-Such
      Abstract: Journal of Information Science, Ahead of Print.
      Cultural heritage institutions have recently started to share their metadata as Linked Open Data (LOD) in order to disseminate and enrich them. The publication of large bibliographic data sets as LOD is a challenge that requires the design and implementation of custom methods for the transformation, management, querying and enrichment of the data. In this report, the methodology defined by previous research for the evaluation of the quality of LOD is analysed and adapted to the specific case of Resource Description Framework (RDF) triples containing standard bibliographic information. The specified quality measures are reported in the case of four highly relevant libraries.
      Citation: Journal of Information Science
      PubDate: 2020-08-04T02:23:50Z
      DOI: 10.1177/0165551520930951
  • The distinctiveness of author interdisciplinarity: A long-neglected issue
           in research on interdisciplinarity
    • Authors: Wenyu Zhang, Shunshun Shi, Xiaoling Huang, Shuai Zhang, Peijia Yao, Yilei Qiu
      Abstract: Journal of Information Science, Ahead of Print.
      In the research on interdisciplinarity (RID), measures for evaluating the interdisciplinarity of scientific entities (e.g., papers, authors, journals or research areas) have been proposed for a long time. The author interdisciplinarity is very different from the other types of interdisciplinarity because of the complex interpersonal relationships between the connected authors. However, previous work has failed to uncover the distinctiveness of author interdisciplinarity and has regarded it as equivalent to other types of interdisciplinarity. In this work, an extended Rao–Stirling diversity measure is proposed, which incorporates the co-author network and a network similarity measure to specifically evaluate the author interdisciplinarity. Moreover, betweenness centrality is used for improving network similarity measure, because of its intrinsic advantage of expressing how an entity loads on different factors in a network, which is highly in line with the characteristic of interdisciplinarity. An experiment on the papers about Public Administration in the Web of Science is conducted; based on the final results, a deeper investigation is performed into by typical authors. The work proposes a novel idea for measuring author interdisciplinarity, which can promote the study of interdisicplinarity measuring in RID.
      Citation: Journal of Information Science
      PubDate: 2020-08-04T02:23:01Z
      DOI: 10.1177/0165551520939499
  • Identification of rumour stances by considering network topology and
           social media comments
    • Authors: Yongcong Luo, Jing Ma, Chai Kiat Yeo
      Abstract: Journal of Information Science, Ahead of Print.
      Online social media (OSM) has become a hotbed for the rapid dissemination of disinformation or faked news. In order to track and limit the spread of faked news, we study stance identification of comments posted on OSM, where the stance can denote the comment’s semantics. In this article, we propose a framework for identification of rumour stances, combining network topology and OSM comments. We construct a vector matrix of comments and words via OTI (optimisation term frequency–inverse document frequency). To better identify the stances, we introduce another vector matrix with novel or special attribute, that is, network topology among the users. Variant autoencoder (VAE) is then applied for dimensionality reduction and optimisation of these vector matrices which are then combined into an integrated matrix [math], tempered by two parameters [math] and [math]. Finally, the matrix is fed into a neural network for final rumour stance identification. Experimental evaluations show that our proposed approach outperforms some state-of-the-art methods and achieves a high precision of 90.26% and F1-score of 88.58%.
      Citation: Journal of Information Science
      PubDate: 2020-07-30T05:31:41Z
      DOI: 10.1177/0165551520944352
  • How do academia and society react to erroneous or deceitful claims'
           The case of retracted articles’ recognition
    • Authors: Hajar Sotudeh, Nilofar Barahmand, Zahra Yousefi, Maryam Yaghtin
      Abstract: Journal of Information Science, Ahead of Print.
      Researchers give credit to peer-reviewed, and thus, credible publications through citations. Despite a rigorous reviewing process, certain articles undergo retraction due to disclosure of their ethical or scientific deficiencies. It is, therefore, important to understand how society and academia react to the erroneous or deceitful claims and purge the science of their unreliable results. Applying a matched-pairs research design, this study examined a sample of medicine-related retracted and non-retracted articles matched by their content similarity. The regression analysis revealed similarities in obsolescence trends of the retracted and non-retracted groups. The Generalized Estimating Equations showed that citations are affected by the retraction status, life after retraction, life cycle and the journals’ previous reputation, with the two formers being the strongest in positively predicting the citations. The retracted papers obtain fewer citations either before or after retraction, implying academia’s watchful reaction to the low-quality papers even before official announcement of their fallibility. They exhibit an equal or higher social recognition level regarding Tweets and Blog Mentions, while a lower status regarding Mendeley Readership. This could signify social users’ sensibility regarding scientific quality since they probably publicise the retraction and warn against the retracted items in their tweets or blogs, while avoiding recording them in their Mendeley profiles. Further scrutiny is required to gain insight into the sensibility, if any, about scientific quality. The study’s originality relies on matching the retracted and non-retracted papers with their topics and neutralising variations in their citation potentials. It is also the first study comparing the groups’ social impacts.
      Citation: Journal of Information Science
      PubDate: 2020-07-30T05:20:15Z
      DOI: 10.1177/0165551520945853
  • Understanding the evolution of a scientific field by clustering and
           visualizing knowledge graphs
    • Authors: Mauro Dalle Lucca Tosi, Julio Cesar dos Reis
      Abstract: Journal of Information Science, Ahead of Print.
      The process of tracking the evolution of a scientific field is arduous. It allows researchers to understand trends in areas of science and predict how they may evolve. Nowadays, most of the automated mechanisms developed to assist researchers in this process do not consider the content of articles to identify changes in its structure, only the articles metadata. These methods are not suited to easily assist researchers to study the concepts that compose an area and its evolution. In this article, we propose a method to track the evolution of a scientific field at a concept level. Our method structures a scientific field using two knowledge graphs, representing distinct periods of the studied field. Then, it clusters them and identifies correspondent clusters between the knowledge graphs, representing the same subareas in distinct time periods. Our solution enables to compare the corresponding clusters, tracking their evolution. We apply and experiment our method in two case studies concerning the artificial intelligence (AI) and the biotechnology (BIO) fields. Findings indicate befitting results regarding the way their evolution can be assessed with our implemented software tool. From our analyses, we perceived evolution in broader subareas of a scientific field, as the growth of the ‘Convolutional Neural Network’ area from 2006; to specific ones, as the decrease of research works using mice to study BRAF-mutation lung cancer from 2018. This work contributes with the development of a web application with interactive user interfaces to assist researchers in representing, analysing and tracking the evolution of scientific fields at a concept level.
      Citation: Journal of Information Science
      PubDate: 2020-07-10T06:10:31Z
      DOI: 10.1177/0165551520937915
  • Does the use of open, non-anonymous peer review in scholarly publishing
           introduce bias' Evidence from the F1000Research post-publication open
           peer review publishing model
    • Authors: Mike Thelwall, Liz Allen, Eleanor-Rose Papas, Zena Nyakoojo, Verena Weigert
      Abstract: Journal of Information Science, Ahead of Print.
      As part of moves towards open knowledge practices, making peer review open is cited as a way to enable fuller scrutiny and transparency of assessments around research. There are now many flavours of open peer review in use across scholarly publishing, including where reviews are fully attributable and the reviewer is named. This study examines whether there is any evidence of bias in two areas of common critique of open, non-anonymous (named) peer review – and used in the post-publication, peer review system operated by the open-access scholarly publishing platform F1000Research. First, is there evidence of potential bias where a reviewer based in a specific country assesses the work of an author also based in the same country' Second, are reviewers influenced by being able to see the comments and know the origins of a previous reviewer' Based on over 4 years of open peer review data, we found some weak evidence that being based in the same country as an author may influence a reviewer’s decision, while there was insufficient evidence to conclude that being able to read an existing published review prior to submitting a review encourages conformity. Thus, while immediate publishing of peer review reports appears to be unproblematic, caution may be needed when selecting same-country reviewers in open systems if other studies confirm these results.
      Citation: Journal of Information Science
      PubDate: 2020-07-06T04:17:13Z
      DOI: 10.1177/0165551520938678
  • Twenty-six years of LIS research focus and hot spots, 1990–2016: A
           co-word analysis
    • Authors: Reza Mokhtarpour, Ali Akbar Khasseh
      Abstract: Journal of Information Science, Ahead of Print.
      The purpose of this research is to map and analyse the conceptual and thematic structure of library and information science (LIS) research from the perspective of the co-word analysis. The bibliographical records consist of all the research papers published in the LIS core journals between 1990 and 2016 and indexed in Web of Science. ‘CiteSpace’ was used to visualise the co-word network of LIS studies. The frequency of co-occurrence and centrality scores in the overall structure of the field showed that the word ‘Science’ is the most significant and pivotal keyword among the nodes in the co-word network of LIS literature, and in this respect, the word ‘Library’ is in the second place. However, the results of the social network analysis uncovered that in spite of the high frequency of the word ‘library’, the pivotal role of the term has been exposed to decline over the time. The results of the analysis of co-word clusters showed that ‘information seeking and retrieval’ is the most important research focus in the intellectual structure of LIS literature during 1990–2016. Also, analysis of the hot spots of the LIS research based on Kleinberg algorithm indicated that the words ‘Internet’ and ‘World Wide Web’ have attracted the most attention by LIS scholars during the years under study.
      Citation: Journal of Information Science
      PubDate: 2020-07-02T08:47:14Z
      DOI: 10.1177/0165551520932119
  • A domain knowledge graph construction method based on Wikipedia
    • Authors: Haoze Yu, Haisheng Li, Dianhui Mao, Qiang Cai
      Abstract: Journal of Information Science, Ahead of Print.
      In order to achieve real-time updating of the domain knowledge graph and improve the relationship extraction ability in the construction process, a domain knowledge graph construction method is proposed. Based on the structured knowledge in Wikipedia’s classification system, we acquire concepts and instances contained in subject areas. A relationship extraction algorithm based on co-word analysis is intended to extract the classification relationships in semi-structured open labels. A Bi-GRU remote supervised relationship extraction model based on a multiple-scale attention mechanism and an improved cross-entropy loss function is proposed to obtain the non-classification relationships of concepts in unstructured texts. Experiments show that the proposed model performs better than the existing methods. Based on the obtained concepts, instances and relationships, a domain knowledge graph is constructed and the domain-independent nodes and relationships contained in them are removed through a vector variance algorithm. The effectiveness of the proposed method is verified by constructing a food domain knowledge graph based on Wikipedia.
      Citation: Journal of Information Science
      PubDate: 2020-06-30T06:16:35Z
      DOI: 10.1177/0165551520932510
  • Modelling users’ perceptions of video information seeking, learning
           through added value and use of curated digital collections
    • Authors: Dan Albertson, Melissa P Johnston
      Abstract: Journal of Information Science, Ahead of Print.
      Information seeking research has provided models of users in the search for information across many different contexts and situations. Digital content curation has emerged as a means for managing information and facilitating user learning by adding ‘value’ to digital content in different ways, enhancing the user experience. Using digital video and K–12 education as the context, this study examined factors representing video information seeking, user learning and use of curated video collections both individually and together as user-centred constructs. Two hundred and fifty-two K–12 teachers provided perceptions of their own information seeking processes and for different qualities of curated content and collections within the context of searching digital video for applied purposes. Results extracted underlying factors of these concepts and demonstrated significant relationships between them. Findings enabled the expansion of a model to incorporate both users’ perceptions of information seeking together with user-centred constructs of learning through added value content and use of curated digital collections. Practical implications of the study help establish baselines for future studies for formulating, incorporating and emphasising added value and video curation qualities based on users’ information seeking within the process.
      Citation: Journal of Information Science
      PubDate: 2020-06-25T04:47:46Z
      DOI: 10.1177/0165551520920807
  • NaLa-Search: A multimodal, interaction-based architecture for faceted
           search on linked open data
    • Authors: José Luis Sánchez-Cervantes, Giner Alor-Hernández, Mario Andrés Paredes-Valverde, Lisbeth Rodríguez-Mazahua, Rafael Valencia-García
      Abstract: Journal of Information Science, Ahead of Print.
      Mobile devices are the technological basis of computational intelligent systems, yet traditional mobile application interfaces tend to rely only on the touch modality. That said, such interfaces could improve human–computer interaction by combining diverse interaction modalities, such as visual, auditory and touch. Also, a lot of information on the Web is published under the Linked Data principles to allow people and computers to share, use and/or reuse high-quality information; however, current tools for searching for, browsing and visualising this kind of data are not fully developed. The goal of this research is to propose a novel architecture called NaLa-Search to effectively explore the Linked Open Data cloud. We present a mobile application that combines voice commands and touch for browsing and searching for such semantic information through faceted search, which is a widely used interaction scheme for exploratory search that is faithful to its richness and practical for real-world use. NaLa-Search was evaluated by real users from the clinical pharmacology domain. In this evaluation, the users had to search and navigate among the DrugBank dataset through voice commands. The evaluation results show that faceted search combined with multiple interaction modalities (e.g. speech and touch) can enhance users’ interaction with semantic knowledge bases.
      Citation: Journal of Information Science
      PubDate: 2020-06-24T04:34:14Z
      DOI: 10.1177/0165551520930918
  • A topic analysis method based on a three-dimensional strategic diagram
    • Authors: Jia Feng, Xiaomin Mu, Wei Wang, Ying Xu
      Abstract: Journal of Information Science, Ahead of Print.
      With the tremendous growth of scientific literature in recent years, methods of detecting and analysing research topics have become more and more important. This study proposes a topic analysis method combining latent Dirichlet allocation (LDA) and a three-dimensional strategic diagram. This study constructs the three-dimensional strategic diagram by three dimensions of centrality, density and novelty, and we classify topics into seven categories according to their strategic positions. Using this topic analysis method, the paper analyses 62,340 publications in the field of medical informatics between 1991 and 2018. Results show that the research scope of medical informatics has become increasingly interdisciplinary. Data analytical methods and technologies are sub-domains with persistent popularity. New health technologies, drug safety, algorithm optimisation and standardisation of medical information are emerging research topics. We hope the findings could help researchers identify potential research topics and facilitate in-depth analysis of the current state of various fields.
      Citation: Journal of Information Science
      PubDate: 2020-06-24T04:32:55Z
      DOI: 10.1177/0165551520930907
  • The impact of semantic annotation techniques on content-based video
           lecture recommendation
    • Authors: Laura Lima Dias, Eduardo Barrére, Jairo Francisco de Souza
      Abstract: Journal of Information Science, Ahead of Print.
      Increasing videos available in educational content repositories makes searching difficult, and recommendation systems have been used to help students and teachers receive a content of interest. Speech is an important carrier of information in video lectures and is used by content-based video recommendation systems. Although automatic speech recognition (ASR) transcripts have been used in modern video recommendation systems, it is not clear how annotation techniques work with noisy text. This article presents an analysis on a set of semantic annotation techniques when applied to text extracted from video lecture speech and their impact on two tasks: annotation and similarity analysis. Experiments show that topic models have good results in this scenario. Besides, a new benchmark for this task has been created and researchers can use it to evaluate new techniques.
      Citation: Journal of Information Science
      PubDate: 2020-06-23T04:31:27Z
      DOI: 10.1177/0165551520931732
  • The effects of globalisation techniques on feature selection for text
    • Authors: Bekir Parlak, Alper Kursat Uysal
      Abstract: Journal of Information Science, Ahead of Print.
      Text classification (TC) is very important and critical task in the 21th century as there exist high volume of electronic data on the Internet. In TC, textual data are characterised by a huge number of highly sparse features/terms. A typical TC consists of many steps and one of the most important steps is undoubtedly feature selection (FS). In this study, we have comprehensively investigated the effects of various globalisation techniques on local feature selection (LFS) methods using datasets with different characteristics such as multi-class unbalanced (MCU), multi-class balanced (MCB), binary-class unbalanced (BCU) and binary-class balanced (BCB). The globalisation techniques used in this study are summation (SUM), weighted-sum (AVG), and maximum (MAX). To investigate the effect of globalisation techniques, we used three LFS methods named as Discriminative Feature Selection (DFSS), odds ratio (OR) and chi-square (CHI2). In the experiments, we have utilised four different benchmark datasets named as Reuters-21578, 20Newsgroup., Enron1, and Polarity in addition to Support Vector Machines (SVM) and Decision Tree (DT) classifiers. According to the experimental results, the most successful globalisation technique is AVG while all situations are taken into account. The experimental results indicate that DFSS method is more successful than OR and CHI2 methods on datasets with MCU and MCB characteristics. However, CHI2 method seems more accurate than OR and DFSS methods on datasets with BCU and BCB characteristics. Also, SVM classifier performed better than DT classifier in most cases.
      Citation: Journal of Information Science
      PubDate: 2020-06-18T01:32:28Z
      DOI: 10.1177/0165551520930897
  • Sentiment analysis of tweets through Altmetrics: A machine learning
    • Authors: Saeed-Ul Hassan, Aneela Saleem, Saira Hanif Soroya, Iqra Safder, Sehrish Iqbal, Saqib Jamil, Faisal Bukhari, Naif Radi Aljohani, Raheel Nawaz
      Abstract: Journal of Information Science, Ahead of Print.
      The purpose of the study is to (a) contribute to annotating an Altmetrics dataset across five disciplines, (b) undertake sentiment analysis using various machine learning and natural language processing–based algorithms, (c) identify the best-performing model and (d) provide a Python library for sentiment analysis of an Altmetrics dataset. First, the researchers gave a set of guidelines to two human annotators familiar with the task of related tweet annotation of scientific literature. They duly labelled the sentiments, achieving an inter-annotator agreement (IAA) of 0.80 (Cohen’s Kappa). Then, the same experiments were run on two versions of the dataset: one with tweets in English and the other with tweets in 23 languages, including English. Using 6388 tweets about 300 papers indexed in Web of Science, the effectiveness of employed machine learning and natural language processing models was measured by comparing with well-known sentiment analysis models, that is, SentiStrength and Sentiment140, as the baseline. It was proved that Support Vector Machine with uni-gram outperformed all the other classifiers and baseline methods employed, with an accuracy of over 85%, followed by Logistic Regression at 83% accuracy and Naïve Bayes at 80%. The precision, recall and F1 scores for Support Vector Machine, Logistic Regression and Naïve Bayes were (0.89, 0.86, 0.86), (0.86, 0.83, 0.80) and (0.85, 0.81, 0.76), respectively.
      Citation: Journal of Information Science
      PubDate: 2020-06-16T04:57:16Z
      DOI: 10.1177/0165551520930917
  • Using social media during job search: The case of 16–24 year olds in
    • Authors: John A Mowbray, Hazel Hall
      Abstract: Journal of Information Science, Ahead of Print.
      Social media are powerful networking platforms that provide users with significant information opportunities. Despite this, little is known about their impact on job search behaviour. Here, interview (participants = 7), focus group (participants = 6) and survey (n = 558) data supplied by young jobseekers in Scotland were analysed to investigate the role of social media in job search. The findings show that Facebook, Twitter and LinkedIn are the most popular platforms for this purpose, and that the type of job sought influences the direction of user behaviour. Frequent social media use for job search is linked with interview invitations. The study also reveals that although most jobseekers use social media for job search sparingly, they are much more likely to do so if advised by a professional. Combined, the findings represent a crucial base of knowledge which can inform careers policy and be used as a platform for further research.
      Citation: Journal of Information Science
      PubDate: 2020-06-11T08:42:31Z
      DOI: 10.1177/0165551520927657
  • From words to connections: Word use similarity as an honest signal
           conducive to employees’ digital communication
    • Authors: Andrea Fronzetti Colladon, Johanne Saint-Charles, Pierre Mongeau
      Abstract: Journal of Information Science, Ahead of Print.
      Bringing together considerations from three research trends (honest signals of collaboration, socio-semantic networks and homophily theory), we hypothesise that word use similarity and having similar social network positions are linked with the level of employees’ digital interaction. To verify our hypothesis, we analyse the communication of close to 1600 employees, interacting on the intranet communication forum of a large company. We study their social dynamics and the ‘honest signals’ that, in past research, proved to be conducive to employees’ engagement and collaboration. We find that word use similarity is the main driver of interaction, much more than other language characteristics or similarity in network position. Our results suggest carefully choosing the language according to the target audience and have practical implications for both company managers and online community administrators. Understanding how to better use language could, for example, support the development of knowledge sharing practices or internal communication campaigns.
      Citation: Journal of Information Science
      PubDate: 2020-06-10T05:11:46Z
      DOI: 10.1177/0165551520929931
  • Supporting information use and task accomplishment: What system features
           do users like and expect'
    • Authors: Jingjing Liu, Yuan Li
      Abstract: Journal of Information Science, Ahead of Print.
      Information systems have been improving in helping users find information. However, they have been less attended to regarding helping searchers in using located information. This research attempts to address the issue of information use by investigating what information systems and features searchers think are helpful in using located information to accomplish information tasks. In all, 32 college students were invited to an information interaction lab, first being interviewed on a recently completed task and then working on a to-be-finished task, both being their real-life tasks of their own choices. Through questionnaires, the study discovered the most favoured existing and expected features helpful for users’ task completion. Users expected convenient citations, note taking in search result pages and being kept on task. Findings in this study have implications on designing search systems that can better support task accomplishment, in addition to returning search results.
      Citation: Journal of Information Science
      PubDate: 2020-06-08T08:00:29Z
      DOI: 10.1177/0165551520917100
  • A survey on automatically constructed universal knowledge bases
    • Authors: Bayzid Ashik Hossain, Abdus Salam, Rolf Schwitter
      Abstract: Journal of Information Science, Ahead of Print.
      A universal knowledge base can be defined as a domain-independent ontology containing instances. Ontologies define the concepts and relations among these concepts and are used to represent a domain of interest. These universal knowledge bases are the elementary units for automated reasoning on the Semantic Web. The Semantic Web is an extension of the World Wide Web which facilitates software agents to share content beyond the limitations of applications and websites. This survey focuses on the most prominent automatically constructed universal knowledge bases including KnowItAll, DBpedia, YAGO, NELL, Probase, BabelNet and Knowledge Vault. We take a closer look at how these knowledge bases are built, in particular at the information extraction and taxonomy generation process and investigate how they are used in practical applications. Due to quality concerns, the most successful and widely employed knowledge bases are manually constructed to maintain high quality, but they suffer from low coverage, high assembly and quality assurance cost. On the contrary, automatic approaches for building knowledge bases try to overcome these drawbacks. Although it is strenuous to achieve the same level of quality as for manual knowledge bases, we found that the surveyed automatically constructed knowledge bases have shown promising results and are useful for many real-world applications.
      Citation: Journal of Information Science
      PubDate: 2020-06-05T06:29:03Z
      DOI: 10.1177/0165551520921342
  • Predicting mobile application breakout using sentiment analysis of
           Facebook posts
    • Authors: Moez Ben Hajhmida, Oumayma Oueslati
      Abstract: Journal of Information Science, Ahead of Print.
      Publishing mobile applications on the official stores is becoming a big business. Many developers are charmed by the billion-dollar success of breakout applications. Thus, in order to ensure success, mobile applications need to sustain top ranking. Previous work on the predictability of mobile applications success aimed to extract from app stores relevant features that influence high rating. In this article, we propose an automated approach to exploit data available on Facebook platform that predicts mobile applications breakout. We collect data from Facebook graph API, then determine sentiment polarity of user comments. We design statistical features to score users sentiment for each post. Then, we compose posts scores with Facebook statistical measures to form a mobile applications breakout dataset. Finally, we use machine learning techniques to build our breakout prediction model. We evaluate our approach with 199 mobile applications and obtain a prediction accuracy of 83.78%. We find that Likes count on a Facebook page is decisive for climbing mobile applications ranking. However, a high rate of negative opinions declines application ranking and deprives mobile application of achieving a breakout. Based on these findings, we provide evidence that user interactions on social networks can influence the success of mobile applications.
      Citation: Journal of Information Science
      PubDate: 2020-05-27T06:31:06Z
      DOI: 10.1177/0165551520917099
  • SBTM: A joint sentiment and behaviour topic model for online course
           discussion forums
    • Authors: Xian Peng, Qinmei Xu, Wenbin Gan
      Abstract: Journal of Information Science, Ahead of Print.
      Large quantities of textual posts are increasingly generated in course discussion forums, and the accumulation of these data greatly increases the cognitive loads on online participants. It is imperative for them to automatically identify the potential semantic information derived from these textual discourse interactions. Moreover, existing topic models can discover the latent topics or sentimental polarities from textual data, but these models typically ignore the interactive ways of discussing topics, thus making it difficult to further construct topics’ semantic space from the perspective of document generation. To solve this issue, we proposed a joint sentiment and behaviour topic model called SBTM, which was an unsupervised approach for automatic analysis of learners’ discussed posts. The results demonstrated that SBTM was quantitatively effective on both model generalisation and topic exploration, and rich topic content was qualitatively characterised. Furthermore, the model can be potentially employed in some practical applications, such as information summarisation and behaviour-oriented personalised recommendation.
      Citation: Journal of Information Science
      PubDate: 2020-05-27T06:23:07Z
      DOI: 10.1177/0165551520917120
  • Intelligent detection of hate speech in Arabic social network: A machine
           learning approach
    • Authors: Ibrahim Aljarah, Maria Habib, Neveen Hijazi, Hossam Faris, Raneem Qaddoura, Bassam Hammo, Mohammad Abushariah, Mohammad Alfawareh
      Abstract: Journal of Information Science, Ahead of Print.
      Nowadays, cyber hate speech is increasingly growing, which forms a serious problem worldwide by threatening the cohesion of civil societies. Hate speech relates to using expressions or phrases that are violent, offensive or insulting for a person or a minority of people. In particular, in the Arab region, the number of Arab social media users is growing rapidly, which is accompanied with high increasing rate of cyber hate speech. This drew our attention to aspire healthy online environments that are free of hatred and discrimination. Therefore, this article aims to detect cyber hate speech based on Arabic context over Twitter platform, by applying Natural Language Processing (NLP) techniques, and machine learning methods. The article considers a set of tweets related to racism, journalism, sports orientation, terrorism and Islam. Several types of features and emotions are extracted and arranged in 15 different combinations of data. The processed dataset is experimented using Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT) and Random Forest (RF), in which RF with the feature set of Term Frequency-Inverse Document Frequency (TF-IDF) and profile-related features achieves the best results. Furthermore, a feature importance analysis is conducted based on RF classifier in order to quantify the predictive ability of features in regard to the hate class.
      Citation: Journal of Information Science
      PubDate: 2020-05-18T07:50:11Z
      DOI: 10.1177/0165551520917651
  • Museum libraries in Spain: A case study at state level
    • Authors: Silvia Cobo-Serrano, Rosario Arquero-Avilés, Gonzalo Marco-Cuenca
      Abstract: Journal of Information Science, Ahead of Print.
      Special libraries are essential information and documentation centres for university teachers and researchers due to the quality and richness of their collections. In Spain, it is estimated that there are 2456 special libraries, although many are unknown either generally or among information professionals. These include museum libraries, which are important centres with valuable collections of bibliographic heritage for the area of Humanities and Social Sciences. The aim of this research is to gain an understanding of the real state of these information units and promote the social value of museum libraries in Spain. To do this, a survey was sent to the libraries of state-owned and -managed museums under the General Directorate of Fine Arts and Cultural Property (Ministry of Culture and Sports) of the Government of Spain. This general objective will be accompanied by a review of the scientific literature on various aspects of museum libraries at national and international level. After addressing the research methodology, the results obtained will be discussed and will include the following topics: collection management, library services and staff, economic and technological resources and finally, library management. Conclusions include recommendations for museum librarians and reveal that institutional cooperation is a strategic issue to improve both museum libraries visibility and their social recognition as cultural and research centre.
      Citation: Journal of Information Science
      PubDate: 2020-05-15T07:08:07Z
      DOI: 10.1177/0165551520917652
  • Performance-based evaluation of academic libraries in the big data era
    • Authors: A Y M Atiquil Islam, Khurshid Ahmad, Muhammad Rafi, Zheng JianMing
      Abstract: Journal of Information Science, Ahead of Print.
      The concept of big data has been extensively considered as a technological modernisation in organisations and educational institutes. Thus, the purpose of this study is to determine whether the modified technology acceptance model (MTAM) is viable for evaluating the performance of librarians in the use of big data analytics in academic libraries. This study used an empirical research method for collecting data from 211 librarians working in Pakistan’s universities. On the basis of the findings of the MTAM analysis by structural equation modelling, the performances of the academic libraries were comprehended through the process of big data. The main influential components of the performance analysis in this study were the big data analytics capabilities, perceived ease of access and the usefulness of big data practices in academic libraries. Subsequently, the utilisation of big data was significantly affected by skills, perceived ease of access and the usefulness of academic libraries. The results also suggested that the various components of the academic libraries lead to effective organisational performance when linked to big data analytics.
      Citation: Journal of Information Science
      PubDate: 2020-05-13T04:34:42Z
      DOI: 10.1177/0165551520918516
  • Semantics-preserving optimisation of mapping multi-column key constraints
           for RDB to RDF transformation
    • Authors: Hee-Gook Jun, Dong-Hyuk Im, Hyoung-Joo Kim
      Abstract: Journal of Information Science, Ahead of Print.
      The relational database (RDB) to resource description framework (RDF) transformation is a major semantic information extraction method because most web data are managed by RDBs. Existing automatic RDB-to-RDF transformation methods generate RDF data without losing the semantics of original relational data. However, two major problems have been observed during the mapping of multi-column key constraints: repetitive data generation and semantic information loss. In this article, we propose an improved RDB-to-RDF transformation method that ensures mapping without the aforementioned problems. Optimised rules are defined to generate an accurate semantic data structure for a multi-column key constraint and to reduce repetitive constraint data. Experimental results show that the proposed method achieves better accuracy in transforming multi-column key constraints and generates compact semantic results without repetitive data.
      Citation: Journal of Information Science
      PubDate: 2020-05-12T05:14:54Z
      DOI: 10.1177/0165551520920804
  • Topic extraction to provide an overview of research activities: The case
           of the high-temperature superconductor and simulation and modelling
    • Authors: Ritsuko Nakajima, Nobuyuki Midorikawa
      Abstract: Journal of Information Science, Ahead of Print.
      For those who are not experts in a particular scientific field, it is difficult to understand scientific research trends. Although studies on the extraction of research trends have been conducted, most focus on extracting global trends from large-scale data, and the methods are often complicated. The purpose of this study is to develop a method of obtaining overviews of a scientific field for non-experts by capturing research trends simply and then to verify the method. To extract research topics which should express research trends, text analysis was performed using abstracts over 12 years of articles on high-temperature superconductors. We characterised three topics for the extracted word groups that frequently occurred. For these topics, we studied their appropriateness using a method that has been little used: examining research articles, review literature and co-citations among research articles used to extract the words, comparisons with controlled index terms assigned to the articles and confirming that there were no contradictions. Based on the established method, we have also applied this method to another research field: ‘simulation and modelling’. Although the method used in this article is simple, important topics were extracted, and the relations with the original articles are clear, which can lead to further investigation of the extracted topics.
      Citation: Journal of Information Science
      PubDate: 2020-05-06T05:11:55Z
      DOI: 10.1177/0165551520920794
  • Partitioning highly, medium and lowly cited publications
    • Authors: Yong Huang, Yi Bu, Ying Ding, Wei Lu
      Abstract: Journal of Information Science, Ahead of Print.
      Dividing papers based on their numbers of citations into several groups constitutes one of the most common research practices in bibliometrics and beyond. However, existing dividing methods are both arbitrary and subject to bias. This article proposes a novel approach to partition highly, medium and lowly cited publications based on their citation distribution. We utilise the whole Web of Science (WoS) dataset to demonstrate how to apply this approach to scholarly datasets and examine the robustness of our algorithm in each of the six disciplines under the WoS dataset. The codes that underlie the algorithm are available online.
      Citation: Journal of Information Science
      PubDate: 2020-04-27T04:30:29Z
      DOI: 10.1177/0165551520917655
  • Which are the influential publications in the Web of Science subject
           categories over a long period of time' CRExplorer software used for
           big-data analyses in bibliometrics
    • Authors: Andreas Thor, Lutz Bornmann, Robin Haunschild, Loet Leydesdorff
      Abstract: Journal of Information Science, Ahead of Print.
      What are the landmark papers in scientific disciplines' Which papers are indispensable for scientific progress' These are typical questions which are of interest not only for researchers (who frequently know the answers – or guess to know them) but also for the interested general public. Citation counts can be used to identify very useful papers since they reflect the wisdom of the crowd – in this case, the scientists using published results for their research. In this study, we identified with recently developed methods for the program CRExplorer landmark publications in nearly all Web of Science subject categories (WoS-SCs). These are publications which belong more frequently than other publications during the citing years to the top-1‰ in their subject area. As examples, we show the results of five subject categories: ‘Information Science & Library Science’, ‘Computer Science, Information Systems’, ‘Computer Science, Software Engineering’, ‘Psychology, Social’ and, ‘Chemistry, Physical’. The results of the other WoS-SCs can be found online at An analyst of the results should keep in mind that the identification of landmark papers depends on the used methods and data. Small differences in methods and/or data may lead to other results.
      Citation: Journal of Information Science
      PubDate: 2020-04-24T05:31:55Z
      DOI: 10.1177/0165551520913817
  • An ensemble clustering approach for topic discovery using implicit text
    • Authors: Muhammad Qasim Memon, Yu Lu, Penghe Chen, Aasma Memon, Muhammad Salman Pathan, Zulfiqar Ali Zardari
      Abstract: Journal of Information Science, Ahead of Print.
      Text segmentation (TS) is the process of dividing multi-topic text collections into cohesive segments using topic boundaries. Similarly, text clustering has been renowned as a major concern when it comes to multi-topic text collections, as they are distinguished by sub-topic structure and their contents are not associated with each other. Existing clustering approaches follow the TS method which relies on word frequencies and may not be suitable to cluster multi-topic text collections. In this work, we propose a new ensemble clustering approach (ECA) is a novel topic-modelling-based clustering approach, which induces the combination of TS and text clustering. We improvised a LDA-onto (LDA-ontology) is a TS-based model, which presents a deterioration of a document into segments (i.e. sub-documents), wherein each sub-document is associated with exactly one sub-topic. We deal with the problem of clustering when it comes to a document that is intrinsically related to various topics and its topical structure is missing. ECA is tested through well-known datasets in order to provide a comprehensive presentation and validation of clustering algorithms using LDA-onto. ECA exhibits the semantic relations of keywords in sub-documents and resultant clusters belong to original documents that they contain. Moreover, present research sheds the light on clustering performances and it indicates that there is no difference over performances (in terms of F-measure) when the number of topics changes. Our findings give above par results in order to analyse the problem of text clustering in a broader spectrum without applying dimension reduction techniques over high sparse data. Specifically, ECA provides an efficient and significant framework than the traditional and segment-based approach, such that achieved results are statistically significant with an average improvement of over 10.2%. For the most part, proposed framework can be evaluated in applications where meaningful data retrieval is useful, such as document summarization, text retrieval, novelty and topic detection.
      Citation: Journal of Information Science
      PubDate: 2020-04-14T08:30:03Z
      DOI: 10.1177/0165551520911590
  • Cross-lingual text similarity exploiting neural machine translation models
    • Authors: Kazuhiro Seki
      Abstract: Journal of Information Science, Ahead of Print.
      This article studies cross-lingual text similarity using neural machine translation models. A straightforward approach based on machine translation is to use translated text so as to make the problem monolingual. Another possible approach is to use intermediate states of machine translation models as recently proposed in the related work, which could avoid propagation of translation errors. We aim at improving both approaches independently and then combine the two types of information, that is, translations and intermediate states, in a learning-to-rank framework to compute cross-lingual text similarity. To evaluate the effectiveness and generalisability of our approach, we conduct empirical experiments on English–Japanese and English–Hindi translation corpora for a cross-lingual sentence retrieval task. It is demonstrated that our approach using translations and intermediate states outperforms other neural network–based approaches and is even comparable with a strong baseline based on a state-of-the-art machine translation system.
      Citation: Journal of Information Science
      PubDate: 2020-03-19T04:39:20Z
      DOI: 10.1177/0165551520912676
  • Semisupervised sentiment analysis method for online text reviews
    • Authors: Gyeong Taek Lee, Chang Ouk Kim, Min Song
      Abstract: Journal of Information Science, Ahead of Print.
      Sentiment analysis plays an important role in understanding individual opinions expressed in websites such as social media and product review sites. The common approaches to sentiment analysis use the sentiments carried by words that express opinions and are based on either supervised or unsupervised learning techniques. The unsupervised learning approach builds a word-sentiment dictionary, but it requires lengthy time periods and high costs to build a reliable dictionary. The supervised learning approach uses machine learning models to learn the sentiment scores of words; however, training a classifier model requires large amounts of labelled text data to achieve a good performance. In this article, we propose a semisupervised approach that performs well despite having only small amounts of labelled data available for training. The proposed method builds a base sentiment dictionary from a small training dataset using a lasso-based ensemble model with minimal human effort. The scores of words not in the training dataset are estimated using an adaptive instance-based learning model. In a pretrained word2vec model space, the sentiment values of the words in the dictionary are propagated to the words that did not exist in the training dataset. Through two experiments, we demonstrate that the performance of the proposed method is comparable to that of supervised learning models trained on large datasets.
      Citation: Journal of Information Science
      PubDate: 2020-03-02T05:46:53Z
      DOI: 10.1177/0165551520910032
  • A qualitative–quantitative study of science mapping by different
           algorithms: The Polish journals landscape
    • Authors: Veslava Osinska
      Abstract: Journal of Information Science, Ahead of Print.
      By applying different clustering algorithms, the author strived to construct the best visual representation of scientific domains and disciplines in Poland. Journals and their disciplinary categories constituted a data set. A comparative analysis of maps was based on both qualitative and quantitative approaches. Complex patterns of eight maps were evaluated taking into account both the local proximity of disciplines and the whole structure of presented domains. Final clustering quality value was introduced and calculated in reference to the knowledge domains. The authors underlined the role of quantitative and qualitative methods in combination in the mapping evaluation. The best results were obtained with the T-distributed stochastic neighbour embedding (t-SNE) algorithm. This youngest technique may have the biggest potential for semantic information studies and in the scope of broadly understood semantic solutions.
      Citation: Journal of Information Science
      PubDate: 2020-02-03T09:30:59Z
      DOI: 10.1177/0165551520902738
  • Online news media website ranking using user-generated content
    • Authors: Samaneh Karimi, Azadeh Shakery, Rakesh Verma
      Abstract: Journal of Information Science, Ahead of Print.
      News media websites are important online resources that have drawn great attention of text mining researchers. The main aim of this study is to propose a framework for ranking online news websites from different viewpoints. The ranking of news websites provides useful information, which can benefit many news-related tasks such as news retrieval and news recommendation. In the proposed framework, the ranking of news websites is obtained by calculating three measures introduced in the article and based on user-generated content (UGC). Each proposed measure is concerned with the performance of news websites from a particular viewpoint including the completeness of news reports, the diversity of events being covered by the website and its speed. The use of UGC in this framework, as a partly unbiased, real-time and low cost content on the web distinguishes the proposed news website ranking framework from the literature. The results obtained for three prominent news websites, British Broadcasting Corporation (BBC), Cable News Network (CNN) and New York Times (NYTimes), show that BBC has the best performance in terms of news completeness and speed, and NYTimes has the best diversity in comparison with the other two websites.
      Citation: Journal of Information Science
      PubDate: 2020-02-03T09:08:28Z
      DOI: 10.1177/0165551519894928
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