Search (2 results, page 1 of 1)

  • × author_ss:"Wang, X."
  • × author_ss:"Zhao, K."
  • × year_i:[2020 TO 2030}
  1. 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) 0.00 
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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
  2. 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) 0.00 
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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

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