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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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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
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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.
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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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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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Song, N.; Cheng, H.; Zhou, H.; Wang, X.: Linking scholarly contents : the design and construction of an argumentation graph (2022)
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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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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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Jiang, Y.; Zheng, H.-T.; Wang, X.; Lu, B.; Wu, K.: Affiliation disambiguation for constructing semantic digital libraries (2011)
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Wang, X.; Hong, Z.; Xu, Y.(C.); Zhang, C.; Ling, H.: Relevance judgments of mobile commercial information (2014)
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Tan, X.; Luo, X.; Wang, X.; Wang, H.; Hou, X.: Representation and display of digital images of cultural heritage : a semantic enrichment approach (2021)
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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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Cui, Y.; Wang, Y.; Liu, X.; Wang, X.; Zhang, X.: Multidimensional scholarly citations : characterizing and understanding scholars' citation behaviors (2023)
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- Abstract
- This study investigates scholars' citation behaviors from a fine-grained perspective. Specifically, each scholarly citation is considered multidimensional rather than logically unidimensional (i.e., present or absent). Thirty million articles from PubMed were accessed for use in empirical research, in which a total of 15 interpretable features of scholarly citations were constructed and grouped into three main categories. Each category corresponds to one aspect of the reasons and motivations behind scholars' citation decision-making during academic writing. Using about 500,000 pairs of actual and randomly generated scholarly citations, a series of Random Forest-based classification experiments were conducted to quantitatively evaluate the correlation between each constructed citation feature and citation decisions made by scholars. Our experimental results indicate that citation proximity is the category most relevant to scholars' citation decision-making, followed by citation authority and citation inertia. However, big-name scholars whose h-indexes rank among the top 1% exhibit a unique pattern of citation behaviors-their citation decision-making correlates most closely with citation inertia, with the correlation nearly three times as strong as that of their ordinary counterparts. Hopefully, the empirical findings presented in this paper can bring us closer to characterizing and understanding the complex process of generating scholarly citations in academia.
