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Empirical studies of online disinformation emphasize matters of public concern such as the COVID-19 pandemic, foreign election interference, and the Russo-Ukraine war, largely in studies that treat the topics separately. Comparatively fewer studies attempt to relate such disparate topics and address the extent to which they share behaviors. In this study, we compare three samples of Twitter data on COVID-19 vaccination, the Ukraine war and the 2022 midterm elections, to ascertain how distinct ideological stances of users across the three samples might be related. Our results indicate the emergence of a broad contrarian stance that is defined by its opposition to public health narratives/policies along with the Biden administration's foreign policy stances. Sharing activity within the contrarian position falls on a spectrum with outright conspiratorial content on one end. We confirm the existence of ideologically coherent cross-subject stances among Twitter users, but in a manner not squarely aligned with right-left political orientations.

Social media are utilized by millions of citizens to discuss important political issues. Politicians use these platforms to connect with the public and broadcast policy positions. Therefore, data from social media has enabled many studies of political discussion. While most analyses are limited to data from individual platforms, people are embedded in a larger information ecosystem spanning multiple social networks. Here we describe and provide access to the Indiana University 2022 U.S. Midterms Multi-Platform Social Media Dataset (MEIU22), a collection of social media posts from Twitter, Facebook, Instagram, Reddit, and 4chan. MEIU22 links to posts about the midterm elections based on a comprehensive list of keywords and tracks the social media accounts of 1,011 candidates from October 1 to December 25, 2022. We also publish the source code of our pipeline to enable similar multi-platform research projects., Comment: 8 pages, 3 figures, forthcoming in ICWSM23

Vaccinations play a critical role in mitigating the impact of COVID-19 and other diseases. This study explores COVID-19 vaccine misinformation circulating on Twitter during 2021, when vaccines were being released to the public in an effort to mitigate the global pandemic. Our findings show a low prevalence of low-credibility information compared to mainstream news. However, most popular low-credibility sources had larger reshare volumes than authoritative sources such as the CDC and WHO. We observed an increasing trend in the prevalence of low-credibility news relative to mainstream news about vaccines. We also observed a considerable amount of suspicious YouTube videos shared on Twitter. Tweets by a small group of about 800 "superspreaders" verified by Twitter accounted for approximately 35% of all reshares of misinformation on the average day, with the top superspreader (@RobertKennedyJr) responsible for over 13% of retweets. Low-credibility news and suspicious YouTube videos were more likely to be shared by automated accounts. Our findings are consistent with the hypothesis that superspreaders are driven by financial incentives that allow them to profit from health misinformation. Despite high-profile cases of deplatformed misinformation superspreaders, our results show that in 2021 a few individuals still played an outsize role in the spread of low-credibility vaccine content. Social media policies should consider revoking the verified status of repeat-spreaders of harmful content, especially during public health crises., Comment: Forthcoming/in press in JMIR

While most social media companies have attempted to address the challenge of COVID-19 misinformation, the success of those policies is difficult to assess, especially when focusing on individual platforms. This study explores the relationship between Twitter and YouTube in spreading COVID-19 vaccine-related misinformation through a mixed-methods approach to analyzing a collection of tweets in 2021 sharing YouTube videos where those Twitter accounts had also linked to deleted YouTube videos. Principal components, cluster and network analyses are used to group the videos and tweets into interpretable groups by shared tweet dates, terms and sharing patterns; content analysis is employed to assess the orientation of tweets and videos to COVID-19 messages. From this we observe that a preponderance of anti-vaccine messaging remains among users who previously shared suspect information, in which a dissident political framing dominates, and which suggests moderation policy inefficacy where the platforms interact.