Though significant efforts such as removing false claims and promoting reliable sources have been increased to combat COVID-19 misinfodemic, it remains an unsolved societal challenge if lacking a proper understanding of susceptible online users, i.e., those who are likely to be attracted by, believe and spread misinformation. This study attempts to answer who constitutes the population vulnerable to the online misinformation in the pandemic, and what are the robust features and short-term behavior signals that distinguish susceptible users from others. Using a 6-month longitudinal user panel on Twitter collected from a geopolitically diverse network-stratified samples in the US, we distinguish different types of users, ranging from social bots to humans with various level of engagement with COVID-related misinformation. We then identify users' online features and situational predictors that correlate with their susceptibility to COVID-19 misinformation. This work brings unique contributions: First, contrary to the prior studies on bot influence, our analysis shows that social bots' contribution to misinformation sharing was surprisingly low, and human-like users' misinformation behaviors exhibit heterogeneity and temporal variability. While the sharing of misinformation was highly concentrated, the risk of occasionally sharing misinformation for average users remained alarmingly high. Second, our findings highlight the political sensitivity activeness and responsiveness to emotionally-charged content among susceptible users. Third, we demonstrate a feasible solution to efficiently predict users' transient susceptibility solely based on their short-term news consumption and exposure from their networks. Our work has an implication in designing effective intervention mechanism to mitigate the misinformation dissipation.
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This content will become publicly available on April 14, 2024
Understanding the Use of Images to Spread COVID-19 Misinformation on Twitter
While COVID-19 text misinformation has already been investigated by various scholars, fewer research efforts have been devoted to characterizing and understanding COVID-19 misinformation that is carried out through visuals like photographs and memes. In this paper, we present a mixed-method analysis of image-based COVID-19 misinformation in 2020 on Twitter. We deploy a computational pipeline to identify COVID-19 related tweets, download the images contained in them, and group together visually similar images. We then develop a codebook to characterize COVID-19 misinformation and manually label images as misinformation or not. Finally, we perform a quantitative analysis of tweets containing COVID-19 misinformation images. We identify five types of COVID-19 misinformation, from a wrong understanding of the threat severity of COVID-19 to the promotion of fake cures and conspiracy theories. We also find that tweets containing COVID-19 misinformation images do not receive more interactions than baseline tweets with random images posted by the same set of users. As for temporal properties, COVID-19 misinformation images are shared for longer periods of time than non-misinformation ones, as well as have longer burst times. %\ywi added "have'' %\ywFor RQ2, we compare non-misinformation images instead of random images, and so it is not a direct comparison. When looking at the users sharing COVID-19 misinformation images on Twitter from the perspective of their political leanings, we find that pro-Democrat and pro-Republican users share a similar amount of tweets containing misleading or false COVID-19 images. However, the types of images that they share are different: while pro-Democrat users focus on misleading claims about the Trump administration's response to the pandemic, as well as often sharing manipulated images intended as satire, pro-Republican users often promote hydroxychloroquine, an ineffective medicine against COVID-19, as well as conspiracy theories about the origin of the virus. Our analysis sets a basis for better understanding COVID-19 misinformation images on social media and the nuances in effectively moderate them.
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- Award ID(s):
- 2200052
- NSF-PAR ID:
- 10460889
- Date Published:
- Journal Name:
- Proceedings of the ACM on Human-Computer Interaction
- Volume:
- 7
- Issue:
- CSCW1
- ISSN:
- 2573-0142
- Page Range / eLocation ID:
- 1 to 32
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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