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The Electronic Library
Journal Prestige (SJR): 0.44

Citation Impact (citeScore): 1
Number of Followers: 1009

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0264-0473 - ISSN (Online) 1758-616X
Published by Emerald

[362 journals]

Unlocking Generation Y’s continuance intentions in personal cloud
storage services: an extended expectation confirmation model analysis
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  Authors: Ali Vafaei-Zadeh, Davoud Nikbin, Jing Loo, Haniruzila Hanifah
  Abstract: This study aims to investigate the factors that influence the continuance intention to use personal cloud storage services among Generation Y. A quantitative online survey was carried out to collect data from 271 respondents. Structural equation modelling with SmartPLS 4.0 software was used to run the analysis and examine the hypothesized relationships in the research model. The study revealed that both satisfaction and habit exert a significant influence on continuance intention, whereas self-efficacy does not demonstrate a significant effect. In addition, satisfaction was found to be influenced by confirmation, perceived usefulness and perceived security. Furthermore, confirmation and cloud storage service quality were found to impact perceived usefulness, while confirmation also had an effect on perceived security. However, the hypothesized moderating role of perceived privacy risk in the relationship between perceived usefulness, perceived security and satisfaction was not supported. This study advances the field by adapting an expanded expectation-confirmation model that delineates the nuanced impacts of habit, user satisfaction and self-efficacy on Generation Y’s continuance intention to use personal cloud storage services. It challenges the conventional wisdom regarding self-efficacy’s influence on technology adoption, offering a more intricate portrayal of its role. This research contributes a distinctive theoretical perspective, emphasizing the complex interplay of factors that inform sustained user engagement with cloud technologies.
  Citation: The Electronic Library
  PubDate: 2024-08-26
  DOI: 10.1108/EL-03-2024-0097
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Multi-dimensional feature fusion-based expert recommendation in community
question answering
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  Authors: Guanghui Ye, Songye Li, Lanqi Wu, Jinyu Wei, Chuan Wu, Yujie Wang, Jiarong Li, Bo Liang, Shuyan Liu
  Abstract: Community question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features. To address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question. The proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features. This work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement. This work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.
  Citation: The Electronic Library
  PubDate: 2024-08-22
  DOI: 10.1108/EL-01-2024-0011
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Low-resource multi-granularity academic function recognition based on
multiple prompt knowledge
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  Authors: Jiawei Liu, Zi Xiong, Yi Jiang, Yongqiang Ma, Wei Lu, Yong Huang, Qikai Cheng
  Abstract: Fine-tuning pre-trained language models (PLMs), e.g. SciBERT, generally require large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining fine-tuning data for scientific NLP tasks is still challenging and expensive. In this paper, the authors propose the mix prompt tuning (MPT), which is a semi-supervised method aiming to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks. Specifically, the proposed method provides multi-perspective representations by combining manually designed prompt templates with automatically learned continuous prompt templates to help the given academic function recognition task take full advantage of knowledge in PLMs. Based on these prompt templates and the fine-tuned PLM, a large number of pseudo labels are assigned to the unlabelled examples. Finally, the authors further fine-tune the PLM using the pseudo training set. The authors evaluate the method on three academic function recognition tasks of different granularity including the citation function, the abstract sentence function and the keyword function, with data sets from the computer science domain and the biomedical domain. Extensive experiments demonstrate the effectiveness of the method and statistically significant improvements against strong baselines. In particular, it achieves an average increase of 5% in Macro-F1 score compared with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised methods under low-resource settings. In addition, MPT is a general method that can be easily applied to other low-resource scientific classification tasks.
  Citation: The Electronic Library
  PubDate: 2024-08-22
  DOI: 10.1108/EL-01-2024-0022
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Tell me what to do first: how behavioural skills drive users’ intention
to adopt online health information
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  Authors: Zhenyi Tang, Pengyi Zhang, Yujia Li, Preben Hansen
  Abstract: To gain a deeper understanding of users’ health information adoption and to promote the effectiveness of health information spread in the context of online limited information, this paper aims to examine how the information-motivation-behavioural (IMB) skills model can be used to organize online health information by experimenting how different IMB elements (information, motivation and behavioural skills) affect users’ intention to adopt health information. The authors conducted an experiment with 48 participants who received health articles with various combinations and sequences of IMB elements, analysing the impact on information adoption intention to share and practice. The authors also examined the mediation effect of information usefulness and the moderating effect of perceived health status. The authors found that: users’ adoption intention of information was influenced by the order of used IMB elements, not the number of elements used; users were more likely to adopt information that started with behavioural skills rather than the model-prescribed IMB sequence; and perceived usefulness mediated the relationship between IMB elements and users’ adoption intention, which means users with different levels of health status all pay more attention to information usefulness and practicability. The study contributes to research on health communication by showing how the IMB model can be applied online to enhance the effectiveness of health information dissemination. It can also help online health communities arrange more effective and engaging health messages to promote users’ willingness to adopt.
  Citation: The Electronic Library
  PubDate: 2024-08-06
  DOI: 10.1108/EL-12-2023-0296
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Readiness of Pakistani journals for open access publishing
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  Authors: Muhammad Zahid Raza, Muhammad Rafiq, Saira Hanif Soroya
  Abstract: This study was designed to discover the readiness of the higher education commission (HEC)-recognized journals of Pakistan in terms of human, financial and technological resources, technical expertise, institutional support, availability of open access (OA) policy, availability of guidance and training, willingness, motivation and so on for OA journal publishing and to expose the challenges in OA journal publishing. A quantitative research approach was used and a structured questionnaire was developed to meet the objectives of this study. A survey method was used to collect data from the editors of all 329 HEC-recognized journals in Pakistan. The respondents of all the HEC-recognized journals of Pakistan are neutral and are not of the view that they have sufficient financial, human, technological/infrastructural resources and technical expertise to continue/initiate an OA journal publishing. ‘No academic reward’; and ‘no monetary reward for the editorial staff’ are both enormous challenges for OA journal publishing. The perceived challenges of OA have a negative impact on readiness for OA publishing. The readiness level of the respondents of the OA journals is higher as compared to the readiness level of the respondents of non-OA journals. This study covered the lists of HEC-recognized journals of 2019. More studies may be conducted based on updated lists of HEC-recognized journals. Qualitative studies may also be conducted to discover the readiness of the HEC-recognized journals of Pakistan for OA journal publishing. This study is the first comprehensive study on this phenomenon and is an effort to fill this gap to invigorate scholarly literature. It may attract the attention of policymakers, funding bodies, parent institutions of the journals and the HEC regarding the readiness of journals in terms of financial, human, technological/infrastructural resources, technical expertise of the journals and challenges of journals to prompt the OA journal publishing paradigm.
  Citation: The Electronic Library
  PubDate: 2024-07-19
  DOI: 10.1108/EL-11-2023-0279
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Research on the construction and application of a smart library services
maturity evaluation system based on CMM
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  Authors: Zhongxian Bai, Lvna Yu, Lei Zhao, Weijia Wang
  Abstract: Smart libraries are the result of the application of smart technologies in the era of digital intelligence. The establishment and improvement of its service evaluation system serve as indicators for evaluating the growth of smart libraries. This study introduces and improves the capability maturity model (CMM), creatively constructs a service maturity model specifically designed for smart libraries and combines the Delphi method with the analytic hierarchy process (AHP) to establish a service maturity evaluation system for smart libraries while calculating indicator weights. Finally, two representative smart libraries are selected as case studies, and an empirical application is conducted using the fuzzy comprehensive evaluation method. The empirical study shows that the developed smart libraries service maturity evaluation system holds significant theoretical and practical value in evaluating smart libraries. Enhances the CMM and creatively constructs a service maturity model for smart libraries. Combines the Delphi method with AHP to establish a service maturity evaluation system while calculating indicator weights. Uses a fuzzy comprehensive evaluation method to evaluate two representative smart libraries. Demonstrates that the smart library services maturity evaluation system holds significant theoretical and practical value.
  Citation: The Electronic Library
  PubDate: 2024-07-18
  DOI: 10.1108/EL-11-2023-0284
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
A method of identifying domain-specific academic user information needs
based on academic Q&A communities
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  Authors: Chunxiu Qin, Yulong Wang, XuBu Ma, Yaxi Liu, Jin Zhang
  Abstract: To address the shortcomings of existing academic user information needs identification methods, such as low efficiency and high subjectivity, this study aims to propose an automated method of identifying online academic user information needs. This study’s method consists of two main parts: the first is the automatic classification of academic user information needs based on the bidirectional encoder representations from transformers (BERT) model. The second is the key content extraction of academic user information needs based on the improved MDERank key phrase extraction (KPE) algorithm. Finally, the applicability and effectiveness of the method are verified by an example of identifying the information needs of academic users in the field of materials science. Experimental results show that the BERT-based information needs classification model achieved the highest weighted average F1 score of 91.61%. The improved MDERank KPE algorithm achieves the highest F1 score of 61%. The empirical analysis results reveal that the information needs of the categories “methods,” “experimental phenomena” and “experimental materials” are relatively high in the materials science field. This study provides a solution for automated identification of academic user information needs. It helps online academic resource platforms to better understand their users’ information needs, which in turn facilitates the platform’s academic resource organization and services.
  Citation: The Electronic Library
  PubDate: 2024-07-11
  DOI: 10.1108/EL-12-2023-0310
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Exploring the relationship between team institutional composition and
novelty in academic papers based on fine-grained knowledge entities
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  Authors: Ziling Chen, Chengzhi Zhang, Heng Zhang, Yi Zhao, Chen Yang, Yang Yang
  Abstract: The composition of author teams is a significant factor affecting the novelty of academic papers. Existing research lacks studies focusing on institutional types and measures of novelty remained at a general level, making it difficult to analyse the types of novelty in papers and to provide a detailed explanation of novelty. This study aims to take the field of natural language processing (NLP) as an example to analyse the relationship between team institutional composition and the fine-grained novelty of academic papers. Firstly, author teams are categorized into three types: academic institutions, industrial institutions and mixed academic and industrial institutions. Next, the authors extract four types of entities from the full paper: methods, data sets, tools and metric. The novelty of papers is evaluated using entity combination measurement methods. Additionally, pairwise combinations of different types of fine-grained entities are analysed to assess their contributions to novel papers. The results of the study found that in the field of NLP, for industrial institutions, collaboration with academic institutions has a higher probability of producing novel papers. From the contribution rate of different types of fine-grained knowledge entities, the mixed academic and industrial institutions pay more attention to the novelty of the combination of method indicators, and the industrial institutions pay more attention to the novelty of the combination of method tools. This paper explores the relationship between the team institutional composition and the novelty of academic papers and reveals the importance of cooperation between industry and academia through fine-grained novelty measurement, which provides key guidance for improving the quality of papers and promoting industry–university–research cooperation.
  Citation: The Electronic Library
  PubDate: 2024-07-09
  DOI: 10.1108/EL-03-2024-0070
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Demystifying oral history with natural language processing and data
analytics: a case study of the Densho digital collection
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  Authors: Haihua Chen, Jeonghyun (Annie) Kim, Jiangping Chen, Aisa Sakata
  Abstract: This study aims to explore the applications of natural language processing (NLP) and data analytics in understanding large-scale digital collections in oral history archives. NLP and data analytics were used to analyse the oral interview transcripts of 904 survivors of the Japanese American incarceration camps collected from Densho Digital Repository, relying specifically on descriptive analysis, keyword extraction, topic modelling and sentiment analysis (SA). The researchers found multiple geographic areas of large residential communities of ethnic Japanese people and the place names of the concentration camps. The keywords and topics extracted reflect the deplorable conditions and militaristic nature of the camps and the forced labour of the internees. When remembering history, the main focus for the narrators remains the redress and reparation movement to obtain the restitution of their civil rights. SA further found that the forcible removal and incarceration of Japanese Americans during Second World War negatively impacted and brought deep trauma to the narrators. This case study demonstrated how NLP and data analytics could be applied to analyse oral history archives and open avenues for discovery. Archival researchers and the general public may benefit from this type of analysis in making connections between temporal, spatial and emotional elements, which will contribute to a holistic understanding of individuals and communities in terms of their collective memory.
  Citation: The Electronic Library
  PubDate: 2024-06-28
  DOI: 10.1108/EL-12-2023-0303
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Developing a model of digital transformation of university libraries based
on meta-synthesis
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  Authors: Soheila Khoeini, Alireza Noruzi, Nader Naghshineh, Fatemeh Sheikhshoaei
  Abstract: The purpose of this study was to develop a model for the digital transformation of university libraries based on meta-synthesis. The approach of this research was qualitative and used the Sandelowski and Barroso’s (2007) seven-step meta-synthesis method to systematically review the literature. The statistical population of the research includes all the scientific publications related to the foundations and dimensions of the digital transformation of university libraries, counting the scientific publications retrieved from six scientific databases in the period from 2004 to 2022, and was based on the critical appraisal skills programme and a screening process. Based on the retrieved publications, 32 documents were selected for further review and analysis. First, a separate code was considered for all the factors extracted from the selected documents, and then, considering the concept of each of the codes, they were categorized into a similar concept. In this way, the research concepts were determined. Based on the analysis done with the help of the content analysis method, there are a total of seven main categories and 24 concepts, including digital culture (including: digital attitude, user-oriented, agility, participation and cooperation, creativity and innovation and learning digital skills of employees), digital librarian (containing: individual competence, knowledge competence, communication competence and skill competence), digital manager (comprising: individual competence, knowledge competence, skill competence), digital services and resources (including, optimally reorganizing library resources, providing digital services to users and providing digital services to the mother organization), digital technologies (containing: digital infrastructure, technological readiness and using digital technologies), support and cooperation of upstream institutions and the mother organization (dealing with: providing human resources, providing technological resources and equipment and making macro policies) and work process and development of digital strategies (comprising: digitalization of processes and development of library digital strategies). Digital transformation is a complex and multi-faceted process, yet it is an indispensable endeavour for university libraries, and managers and librarians cannot be expected to be familiar with these concepts without training or study and then be able to move in the right direction towards the digital transformation of libraries. To the best of the authors’ knowledge, this is the first study to develop a model for the digital transformation of university libraries. The results of this research indicate the effective factors and requirements needed for the digital transformation of university libraries and address the importance of understanding the concepts for managers and librarians.
  Citation: The Electronic Library
  PubDate: 2024-06-24
  DOI: 10.1108/EL-02-2024-0046
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
A study on the influence of digital literacy on elderly user’s intention
to identify social media false information
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  Authors: Lu Chen, Jing Jia, Manling Xiao, Chengzhen Wu, Luwen Zhang
  Abstract: This research exclusively focuses on China’s elderly Internet users given how severe a threat disinformation has become for this particular population group as social media platforms thrive and the number of elderly netizens grows in China. The purpose of this study is to explore the mechanism of how elderly social media users’ intention to identify false information is influenced helps supplement the knowledge system of false information governance and provides a basis for correction practices. This study focuses on the digital literacy of elderly social media users and builds a theoretical model of their intention to identify false information based on the theory of planned behaviour. It introduces two variables – namely, risk perception and self-efficacy – and clarifies the relationships between the variables. Questionnaires were distributed both online and offline, with a total of 468 collected. A structural equation model was built for empirical analysis. The results show that digital literacy positively influences risk perception, self-efficacy, subjective norms and perceived behavioural control. Risk perception positively influences subjective norms, perceived behavioural control and the attitude towards the identification of false information. Self-efficacy positively influences perceived behavioural control but does not significantly impact the intention to identify. Subjective norms positively influence the attitude towards identification and the intention to identify. Perceived behavioural control positively influences the attitude towards identification but does not significantly impact the intention to identify. The attitude towards identification positively influences the intention to identify. Based on relevant theories and the results of the empirical analysis, this study provides suggestions for false information governance from the perspectives of social media platform collaboration and elderly social media users.
  Citation: The Electronic Library
  PubDate: 2024-06-24
  DOI: 10.1108/EL-10-2023-0257
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Factors influencing the adoption of big data in libraries: a systematic
literature review of peer-reviewed articles from 2013 to 2023
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  Authors: Khurram Shahzad, Shakeel Ahmad Khan
  Abstract: The purpose of this study are to identify the factors influencing the adoption of big data in libraries, determine the challenges causing the hindrance of big data implementation and reveal the best practices for the efficient adoption of big data in libraries. A systematic literature review was applied to address the objectives of the study. Twenty-two studies published in peer-reviewed journals were selected to conduct the study. The findings of the study revealed that decision-making, service enhancement, professional development and preservation factors influenced the adoption of big data technologies in libraries. The study also displayed that challenges of infrastructure, technical skills, data management and legal considerations caused barriers to the adoption of big data in libraries. Results also revealed that training and professional development, guidelines and policies establishment, leadership and strategic planning and resource allocation proved fruitful in the efficient adoption of big data applications in libraries. The study offers theoretical implications for future investigators through the provision of innovative literature on the factors, challenges and best practices associated with big data in the context of librarianship. The study has also provided practical implications for management bodies by offering guidelines for the successful adoption of big data in libraries.
  Citation: The Electronic Library
  PubDate: 2024-06-18
  DOI: 10.1108/EL-02-2024-0057
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Evidence of the conditions affecting knowledge hiding among librarians in
academic libraries
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  Authors: Mojtaba Kaffashan Kakhki, Ambika Zutshi, Shabnam Refoua, Iman Maleksadati, Hassan Behzadi
  Abstract: This study aims to identify and theorize the conditions affecting the formation of librarians’ knowledge-hiding behaviour in academic libraries. This study is a qualitative research based on the grounded theory approach. The data collection method involved in-depth, semi-structured interviews. The data was analysed using the MAXQDA software in three stages: open, axial and selective coding. The study included 22 faculty members and experienced librarians from academic libraries. The participants were selected using a combination of targeted and snowball sampling techniques. The study yielded 96 open codes, 24 axial codes and 18 selective general codes related to the axial category of knowledge hiding (KH). The librarians’ KH axial coding paradigm pattern was developed in an academic library setting. The study also highlighted some general consequences of KH in academic libraries, such as lobbying and creating knowledge rents, deterioration of organizational relationships and interactions, reducing the competitive advantage of academic libraries and hindering individual and organizational learning. This study has made a valuable contribution to the identification and explanation of the factors that affect KH. In addition, it has filled a research gap within the library and information science (LIS) field. The findings of the study may offer managers new strategies for addressing the occurrence of KH in academic libraries, and they also add to the existing literature on knowledge management in LIS.
  Citation: The Electronic Library
  PubDate: 2024-05-31
  DOI: 10.1108/EL-09-2023-0229
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Impact of information accessibility and diagnosticity on eye movements of
children searching for information
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  Authors: Jia-Rui Sun, Ko-Chiu Wu
  Abstract: This study aims to explore the eye movement behavior of preadolescent children accessing and diagnosing information. The researchers tracked the eye movements of 30 children with an eye-tracking apparatus. Using the kit of factor-referenced cognitive tests to measure perceptual speed and associative memory, they measured information-searching behavior with screen recordings, the data of which were analyzed by IBM SPSS Statistics 26. Regarding information accessibility, there was a correlation between the child’s age, associative memory and the number of round-trip choices, and there were differences in the total fixation area among children of different age groups. Regarding diagnosticity, perceptual speed was positively correlated with the total fixation area, and the number of round-trip choices was negatively correlated with fixation duration. Empirical evidence suggests that during information encoding, perceptual speed is the most important influencing factor. Extensive research indicates that children predominantly rely on recall and familiarity when searching for new information, both of which play roles in associative memory. Through an examination of the psychological and behavioral indicators of children, the study elucidated the cognitive processes involved in information processing and how children engage with information at both visual and cognitive levels.
  Citation: The Electronic Library
  PubDate: 2024-05-31
  DOI: 10.1108/EL-10-2023-0255
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Recommended information adoption by university students in their
study/research
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  Authors: Xianjin Zha, Zeyu Lu, Yalan Yan
  Abstract: For undergraduate and graduate students in universities, social media are playing an important role in their study/research because a large amount of academic information has been accumulated on social media. Indeed, social media are complementing university libraries. Given that intelligent recommender systems have been widely implemented on social media, this paper aims to examine the adoption mechanism of intelligently recommended information by university students in their study/research. Building upon the updated information system success model and herding theory, this study developed a research model to examine the determinants of recommended information adoption in mobile applications for social media. Data were collected through an online questionnaire and analyzed with partial least squares structural equation modelling. The results suggest that herding belief is a valid second-order construct, comprising two first-order dimensions of imitating others and discounting their own information. Information quality, system quality and service quality directly impact satisfaction with the intelligent recommender system. Furthermore, satisfaction with the intelligent recommender system and herding belief directly impact recommended information adoption by university students in their study/research. This study draws on the updated information system success model and incorporates herding belief as an extended component to investigate recommended information adoption, providing a new lens for understanding recommended information adoption by university students in their study/research.
  Citation: The Electronic Library
  PubDate: 2024-05-29
  DOI: 10.1108/EL-12-2023-0312
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Risk identification of public opinion on social media: a new approach
based on cross-spatial network analysis
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  Authors: Yiming Li, Xukan Xu, Muhammad Riaz, Yifan Su
  Abstract: This study aims to use geographical information on social media for public opinion risk identification during a crisis. This study constructs a double-layer network that associates the online public opinion with geographical information. In the double-layer network, Gaussian process regression is used to train the prediction model for geographical locations. Second, cross-space information flow is described using local government data availability and regional internet development indicators. Finally, the structural characteristics and information flow of the double-layer network are explored to capture public opinion risks in a fine-grained manner. This study used the early stages of the COVID-19 outbreak for validation analyses, and it collected more than 90,000 pieces of public opinion data from microblogs. In the early stages of the COVID-19 outbreak, the double-layer network exhibited a radiating state, and the information dissemination was more dependent on the nodes with higher in-degree. Moreover, the double-layer network structure showed geographical differences. The risk contagion was more significant in areas where information flow was prominent, but the influence of nodes was reduced. Public opinion risk identification that incorporates geographical scenarios contributes to enhanced situational awareness. This study not only effectively extends geographical information on social media, but also provides valuable insights for accurately responding to public opinion.
  Citation: The Electronic Library
  PubDate: 2024-05-20
  DOI: 10.1108/EL-09-2023-0208
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Sentiment time series clustering of Danmu videos based on BERT fine-tuning
and SBD-K-shape
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  Authors: Ruoxi Zhang, Chenhan Ren
  Abstract: This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering. This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data. The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others. Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.
  Citation: The Electronic Library
  PubDate: 2024-04-22
  DOI: 10.1108/EL-10-2023-0243
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Exploring and evaluating the index for interdisciplinary breakthrough
innovation detection
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  Authors: Zhongyi Wang, Xueyao Qiao, Jing Chen, Lina Li, Haoxuan Zhang, Junhua Ding, Haihua Chen
  Abstract: This study aims to establish a reliable index to identify interdisciplinary breakthrough innovation effectively. We constructed a new index, the DDiv index, for this purpose. The DDiv index incorporates the degree of interdisciplinarity in the breakthrough index. To validate the index, a data set combining the publication records and citations of Nobel Prize laureates was divided into experimental and control groups. The validation methods included sensitivity analysis, correlation analysis and effectiveness analysis. The sensitivity analysis demonstrated the DDiv index’s ability to differentiate interdisciplinary breakthrough papers from various categories of papers. This index not only retains the strengths of the existing index in identifying breakthrough innovation but also captures interdisciplinary characteristics. The correlation analysis revealed a significant correlation (correlation coefficient = 0.555) between the interdisciplinary attributes of scientific research and the occurrence of breakthrough innovation. The effectiveness analysis showed that the DDiv index reached the highest prediction accuracy of 0.8. Furthermore, the DDiv index outperforms the traditional DI index in terms of accuracy when it comes to identifying interdisciplinary breakthrough innovation. This study proposed a practical and effective index that combines interdisciplinary and disruptive dimensions for detecting interdisciplinary breakthrough innovation. The identification and measurement of interdisciplinary breakthrough innovation play a crucial role in facilitating the integration of multidisciplinary knowledge, thereby accelerating the scientific breakthrough process.
  Citation: The Electronic Library
  PubDate: 2024-04-10
  DOI: 10.1108/EL-06-2023-0141
  Issue No: Vol. ahead-of-print, No. ahead-of-print (2024)
Research on intangible cultural heritage resource description and
knowledge fusion based on linked data
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  Authors: Qing Fan
  Abstract: The purpose of this article is to contribute to the digital development and utilization of China’s intangible cultural heritage resources, research on the theft of intangible cultural heritage resources and knowledge integration based on linked data is proposed to promote the standardized description of intangible cultural heritage knowledge and realize the digital dissemination and development of intangible cultural heritage. In this study, firstly, the knowledge organization theory and semantic Web technology are used to describe the intangible cultural heritage digital resource objects in metadata specifications. Secondly, the ontology theory and technical methods are used to build a conceptual model of the intangible cultural resources field and determine the concept sets and hierarchical relationships in this field. Finally, the semantic Web technology is used to establish semantic associations between intangible cultural heritage resource knowledge. The study findings indicate that the knowledge organization of intangible cultural heritage resources constructed in this study provides a solution for the digital development of intangible cultural heritage in China. It also provides semantic retrieval with better knowledge granularity and helps to visualize the knowledge content of intangible cultural heritage. This study summarizes and provides significant theoretical and practical value for the digital development of intangible cultural heritage and the resource description and knowledge fusion of intangible cultural heritage can help to discover the semantic relationship of intangible cultural heritage in multiple dimensions and levels.
  Citation: The Electronic Library
  PubDate: 2023-12-06
  DOI: 10.1108/EL-01-2023-0018
  Issue No: Vol. 42, No. 4 (2023)