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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

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Abstract

Performance assessment is ubiquitous and crucial in people analytics. Scientific impact, particularly, plays a significant role in the academia. This paper attempts to understand researchers' career trajectories by considering the research community as a social network, where individuals build ties with each other via coauthorship. The resulting linkage facilitates information flow and affects researchers' future impact. Consequently, we systematically investigate the career trajectories of researchers with respect to research impact using the social capital theory as our theoretical foundation. Specifically, for early-stage and mid-career academics, we find that connections with prominent researchers associate with greater impact. Brokerage positions, in addition, are beneficial to a researcher's research impact in the long run. For senior researchers, however, the only social network feature that significantly affects their future impact is the reputation of their recently built ties. Finally, we build predictive models on future research impact which can be leveraged by both organizations and individuals. This paper provides empirical evidence for how social networks provide signals on researchers' career dynamics guided by social capital theory. Our findings have implications for individual researchers to strategically plan and promote their careers and for research institutions to better evaluate current as well as prospective employees.

Source

Journal of the Association for Information Science and Technology. 72(2021) no.4, S.454-472

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Source

Journal of the Association for Information Science and Technology. 72(2021) no.6, S.710-722