-
Wang, X.; Zhang, M.; Fan, W.; Zhao, K.: Understanding the spread of COVID-19 misinformation on social media : the effects of topics and a political leader's nudge (2022)
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- Abstract
- The spread of misinformation on social media has become a major societal issue during recent years. In this work, we used the ongoing COVID-19 pandemic as a case study to systematically investigate factors associated with the spread of multi-topic misinformation related to one event on social media based on the heuristic-systematic model. Among factors related to systematic processing of information, we discovered that the topics of a misinformation story matter, with conspiracy theories being the most likely to be retweeted. As for factors related to heuristic processing of information, such as when citizens look up to their leaders during such a crisis, our results demonstrated that behaviors of a political leader, former US President Donald J. Trump, may have nudged people's sharing of COVID-19 misinformation. Outcomes of this study help social media platform and users better understand and prevent the spread of misinformation on social media.
- Source
- Journal of the Association for Information Science and Technology. 73(2022) no.5, S.726-737
-
Wang, X.; High, A.; Wang, X.; Zhao, K.: Predicting users' continued engagement in online health communities from the quantity and quality of received support (2021)
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- Abstract
- This article presents a rare insight into the migration of municipality record-keeping databases. The migration of a database for preservation purposes poses Online health communities (OHCs) have been major resources for people with similar health concerns to interact with each other. They offer easily accessible platforms for users to seek, receive, and provide supports by posting. Taking the advantage of text mining and machine learning techniques, we identified social support type(s) in each post and a new user's support needs in an OHC. We examined a user's first-time support-seeking experience by measuring both quantity and quality of received support. Our results revealed that the amount and match of received support are positive and significant predictors of new users' continued engagement. Our outcomes can provide insight for designing and managing a sustainable OHC by retaining users.
- Source
- Journal of the Association for Information Science and Technology. 72(2021) no.6, S.710-722
-
Fang, Z.; Costas, R.; Tian, W.; Wang, X.; Wouters, P.: How is science clicked on Twitter? : click metrics for Bitly short links to scientific publications (2021)
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- Abstract
- To provide some context for the potential engagement behavior of Twitter users around science, this article investigates how Bitly short links to scientific publications embedded in scholarly Twitter mentions are clicked on Twitter. Based on the click metrics of over 1.1 million Bitly short links referring to Web of Science (WoS) publications, our results show that around 49.5% of them were not clicked by Twitter users. For those Bitly short links with clicks from Twitter, the majority of their Twitter clicks accumulated within a short period of time after they were first tweeted. Bitly short links to the publications in the field of Social Sciences and Humanities tend to attract more clicks from Twitter over other subject fields. This article also assesses the extent to which Twitter clicks are correlated with some other impact indicators. Twitter clicks are weakly correlated with scholarly impact indicators (WoS citations and Mendeley readers), but moderately correlated to other Twitter engagement indicators (total retweets and total likes). In light of these results, we highlight the importance of paying more attention to the click metrics of URLs in scholarly Twitter mentions, to improve our understanding about the more effective dissemination and reception of science information on Twitter.
- Source
- Journal of the Association for Information Science and Technology. 72(2021) no.7, S.918-932
-
Tian, W.; Cai, R.; Fang, Z.; Geng, Y.; Wang, X.; Hu, Z.: Understanding co-corresponding authorship : a bibliometric analysis and detailed overview (2024)
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- Abstract
- The phenomenon of co-corresponding authorship is becoming more and more common. To understand the practice of authorship credit sharing among multiple corresponding authors, we comprehensively analyzed the characteristics of the phenomenon of co-corresponding authorships from the perspectives of countries, disciplines, journals, and articles. This researcher was based on a dataset of nearly 8 million articles indexed in the Web of Science, which provides systematic, cross-disciplinary, and large-scale evidence for understanding the phenomenon of co-corresponding authorship for the first time. Our findings reveal that higher proportions of co-corresponding authorship exist in Asian countries, especially in China. From the perspective of disciplines, there is a relatively higher proportion of co-corresponding authorship in the fields of engineering and medicine, while a lower proportion exists in the humanities, social sciences, and computer science fields. From the perspective of journals, high-quality journals usually have higher proportions of co-corresponding authorship. At the level of the article, our findings proved that, compared to articles with a single corresponding author, articles with multiple corresponding authors have a significant citation advantage.
- Source
- Journal of the Association for Information Science and Technology. 75(2023) no.1, S.3-23
-
Walsh, J.A.; Cobb, P.J.; Fremery, W. de; Golub, K.; Keah, H.; Kim, J.; Kiplang'at, J.; Liu, Y.-H.; Mahony, S.; Oh, S.G.; Sula, C.A.; Underwood, T.; Wang, X.: Digital humanities in the iSchool (2022)
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- Abstract
- The interdisciplinary field known as digital humanities (DH) is represented in various forms in the teaching and research practiced in iSchools. Building on the work of an iSchools organization committee charged with exploring digital humanities curricula, we present findings from a series of related studies exploring aspects of DH teaching, education, and research in iSchools, often in collaboration with other units and disciplines. Through a survey of iSchool programs and an online DH course registry, we investigate the various education models for DH training found in iSchools, followed by a detailed look at DH courses and curricula, explored through analysis of course syllabi and course descriptions. We take a brief look at collaborative disciplines with which iSchools cooperate on DH research projects or in offering DH education. Next, we explore DH careers through an analysis of relevant job advertisements. Finally, we offer some observations about the management and administrative challenges and opportunities related to offering a new iSchool DH program. Our results provide a snapshot of the current state of digital humanities in iSchools which may usefully inform the design and evolution of new DH programs, degrees, and related initiatives.
- Source
- Journal of the Association for Information Science and Technology. 73(2022) no.2, S.188-203
-
Ding, Y.; Zhang, G.; Chambers, T.; Song, M.; Wang, X.; Zhai, C.: Content-based citation analysis : the next generation of citation analysis (2014)
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- Date
- 22. 8.2014 16:52:04
- Source
- Journal of the Association for Information Science and Technology. 65(2014) no.9, S.1820-1833
-
Teo, H.-H.; Wang, X.; Wei, K.-K.; Sia, C.-L.; Lee, M.K.O.: Organizational learning capacity and attitude toward complex technological innovations : an empirical study (2006)
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- Abstract
- Recent studies have found organizational learning capacity to be a key factor in influencing organizational assimilation and exploitation of knowledge-intensive innovations. Despite its increasing importance, the impact of organizational learning capacity an technology assimilation is not well understood. Distilling from extant works an organizational learning and technology assimilation, this study identifies four components of organizational learning capacity, namely, systems orientation, organizational climate for learning orientation, knowledge acquisition and utilization orientation, and information sharing and dissemination orientation. The authors subject these components to structural equation modeling analyses to better understand their structure and dimensionality. The analyses strongly support the proposed four major dimensions underlying organizational learning capacity. Organizational learning capacity, as a higher-order factor, has a significant impact an attitude towards organizational adoption of knowledge-intensive innovations. Implications for practice and research are discussed.
- Source
- Journal of the American Society for Information Science and Technology. 57(2006) no.2, S.264-279
-
Jiang, Y.; Zheng, H.-T.; Wang, X.; Lu, B.; Wu, K.: Affiliation disambiguation for constructing semantic digital libraries (2011)
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- Source
- Journal of the American Society for Information Science and Technology. 62(2011) no.6, S.1029-1041
-
Wang, X.; Hong, Z.; Xu, Y.(C.); Zhang, C.; Ling, H.: Relevance judgments of mobile commercial information (2014)
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- Source
- Journal of the Association for Information Science and Technology. 65(2014) no.7, S.1335-1348
-
Wang, X.; Song, N.; Zhou, H.; Cheng, H.: Argument ontology for describing scientific articles : a statistical study based on articles from two research areas (2019)
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- Source
- Proceedings of the Association for Information Science and Technology 56(2019) no.1, S.855-857
-
Wang, X.; Erdelez, S.; Allen, C.; Anderson, B.; Cao, H.; Shyu, C.-R.: Role of domain knowledge in developing user-centered medical-image indexing (2012)
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- Source
- Journal of the American Society for Information Science and Technology. 63(2012) no.2, S.225-241
-
Reyes Ayala, B.; Knudson, R.; Chen, J.; Cao, G.; Wang, X.: Metadata records machine translation combining multi-engine outputs with limited parallel data (2018)
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- Source
- Journal of the Association for Information Science and Technology. 69(2018) no.1, S.47-59
-
Yang, B.; Rousseau, R.; Wang, X.; Huang, S.: How important is scientific software in bioinformatics research? : a comparative study between international and Chinese research communities (2018)
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- Source
- Journal of the Association for Information Science and Technology. 69(2018) no.9, S.1122-1133
-
Wang, F.; Wang, X.: Tracing theory diffusion : a text mining and citation-based analysis of TAM (2020)
0.00
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- Abstract
- Theory is a kind of condensed human knowledge. This paper is to examine the mechanism of interdisciplinary diffusion of theoretical knowledge by tracing the diffusion of a representative theory, the Technology Acceptance Model (TAM). Design/methodology/approach Based on the full-scale dataset of Web of Science (WoS), the citations of Davis's original work about TAM were analysed and the interdisciplinary diffusion paths of TAM were delineated, a supervised machine learning method was used to extract theory incidents, and a content analysis was used to categorize the patterns of theory evolution. Findings It is found that the diffusion of a theory is intertwined with its evolution. In the process, the role that a participating discipline play is related to its knowledge distance from the original disciplines of TAM. With the distance increases, the capacity to support theory development and innovation weakens, while that to assume analytical tools for practical problems increases. During the diffusion, a theory evolves into new extensions in four theoretical construction patterns, elaboration, proliferation, competition and integration. Research limitations/implications The study does not only deepen the understanding of the trajectory of a theory but also enriches the research of knowledge diffusion and innovation. Originality/value The study elaborates the relationship between theory diffusion and theory development, reveals the roles of the participating disciplines played in theory diffusion and vice versa, interprets four patterns of theory evolution and uses text mining technique to extract theory incidents, which makes up for the shortcomings of citation analysis and content analysis used in previous studies.
-
Wang, X.; Duan, Q.; Liang, M.: Understanding the process of data reuse : an extensive review (2021)
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- Source
- Journal of the Association for Information Science and Technology. 72(2021) no.9, S.1161-1182
-
Wang, X.; Song, N.; Zhou, H.; Cheng, H.: ¬The representation of argumentation in scientific papers : a comparative analysis of two research areas (2022)
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- Source
- Journal of the Association for Information Science and Technology. 73(2022) no.6, S.863-878
-
Cui, Y.; Wang, Y.; Liu, X.; Wang, X.; Zhang, X.: Multidimensional scholarly citations : characterizing and understanding scholars' citation behaviors (2023)
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- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.1, S.115-127
-
Wang, X.; Lin, X.; Shao, B.: Artificial intelligence changes the way we work : a close look at innovating with chatbots (2023)
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- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.3, S.339-353
