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1. Understanding the Use of Images to Spread COVID-19 Misinformation on Twitter

   https://doi.org/10.1145/3579542  

   Wang, Yuping; Ling, Chen; Stringhini, Gianluca (April 2023, Proceedings of the ACM on Human-Computer Interaction)

   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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2. Characterizing User Susceptibility to COVID-19 Misinformation on Twitter

   Teng, Xian; Lin, Yu-Ru; Chung, Wen-Ting; Li, Ang; Kovashka, Adriana (January 2022, Proceedings of the International AAAI Conference on Weblogs and Social Media)

   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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3. Pandemic Culture Wars: Partisan Differences in the Moral Language of COVID-19 Discussions

   Rao, Ashwin; Guo, Siyi; Wang, Nina; Morstatter, Fred; Lerman, Kristina (May 2023, arXivorg)

   Effective response to pandemics requires coordinated adoption of mitigation measures, like masking and quarantines, to curb a virus's spread. However, as the COVID-19 pandemic demonstrated, political divisions can hinder consensus on the appropriate response. To better understand these divisions, our study examines a vast collection of COVID-19-related tweets. We focus on five contentious issues: coronavirus origins, lockdowns, masking, education, and vaccines. We describe a weakly supervised method to identify issue-relevant tweets and employ state-of-the-art computational methods to analyze moral language and infer political ideology. We explore how partisanship and moral language shape conversations about these issues. Our findings reveal ideological differences in issue salience and moral language used by different groups. We find that conservatives use more negatively-valenced moral language than liberals and that political elites use moral rhetoric to a greater extent than non-elites across most issues. Examining the evolution and moralization on divisive issues can provide valuable insights into the dynamics of COVID-19 discussions and assist policymakers in better understanding the emergence of ideological divisions. 

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4. Examining the impact of sharing COVID-19 misinformation online on mental health

   https://doi.org/10.1038/s41598-022-11488-y  

   Verma, Gaurav; Bhardwaj, Ankur; Aledavood, Talayeh; De Choudhury, Munmun; Kumar, Srijan (December 2022, Scientific Reports)

   Abstract Misinformation about the COVID-19 pandemic proliferated widely on social media platforms during the course of the health crisis. Experts have speculated that consuming misinformation online can potentially worsen the mental health of individuals, by causing heightened anxiety, stress, and even suicidal ideation. The present study aims to quantify the causal relationship between sharing misinformation, a strong indicator of consuming misinformation, and experiencing exacerbated anxiety. We conduct a large-scale observational study spanning over 80 million Twitter posts made by 76,985 Twitter users during an 18.5 month period. The results from this study demonstrate that users who shared COVID-19 misinformation experienced approximately two times additional increase in anxiety when compared to similar users who did not share misinformation. Socio-demographic analysis reveals that women, racial minorities, and individuals with lower levels of education in the United States experienced a disproportionately higher increase in anxiety when compared to the other users. These findings shed light on the mental health costs of consuming online misinformation. The work bears practical implications for social media platforms in curbing the adverse psychological impacts of misinformation, while also upholding the ethos of an online public sphere. 

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5. Explaining Twitter’s inability to effectively moderate content during the COVID-19 pandemic

   https://doi.org/10.1038/s41598-025-20033-6  

   Broniatowski, David A; Zhong, Wei; Simons, Joseph R; Jamison, Amelia M; Dredze, Mark; Abroms, Lorien C (October 2025, Scientific Reports)

   Abstract Social media platforms routinely face pressure to restrict harmful content while protecting free speech; however, prior theory suggests that platform design might undermine the efficacy of content moderation. During the COVID-19 pandemic, major social media platforms removed content that violated their medical misinformation policies. Although controversial, it is widely assumed that these interventions, such as deplatforming and content removal, are efficacious; however, this claim has not been evaluated based on evidence. We therefore evaluated the efficacy of Twitter’s attempts to curtail vaccine misinformation during the COVID-19 pandemic. We found that several clusters of vaccine skeptical accounts generated a larger share of tweets about vaccines, increased in virality, and continued to spread low-quality and likely misinformative content despite Twitter’s sustained removal of content and accounts — both when compared to prior trends and to corresponding clusters of pro-vaccine accounts. Several of these accounts were subject to a contemporaneous moderation action, in which Twitter removed 70,000 accounts on January 8, 2021. Although this action reduced activity among targeted accounts, virality increased and information quality decreased among both these accounts and those sharing similar political affiliations, calling into question the efficacy of these removals. Novel platforms that share Twitter’s architecture may therefore face similar moderation challenges. 

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