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Social media platforms are increasingly using community-based verification systems, such as Community Notes, and AI systems to flag and contextualize potentially misleading content at scale. While these approaches promise speed and broad coverage, concerns about accuracy, bias, and transparency persist. Drawing on interviews with 29 fact checkers, we find that practitioners see community-based verification and AI Note Writers as complementary tools that can support, but not replace, professional fact-checking.

This research note investigates the aftermath of YouTube’s global ban on Russian state-affiliated media channels in the wake of Russia’s full-scale invasion of Ukraine in 2022. Using over 12 million YouTube comments across 40 Russian-language channels, we analyzed the effectiveness of the ban and the shifts in user activity before and after the platform’s intervention.

A significant body of research is dedicated to developing language models that can detect various types of online abuse, for example, hate speech, cyberbullying. However, there is a disconnect between platform policies, which often consider the author’s intention as a criterion for content moderation, and the current capabilities of detection models, which typically lack efforts to capture intent.

Observed corrections of misinformation on social media can encourage more accurate beliefs, but for these benefits to occur, corrections must happen. By exploring people’s perceptions of witnessing and performing corrections on social media, we find that many people say they observe and perform corrections across the United States, the United Kingdom, Canada, and France.

This study examines the experiences of those who participate in bottom-up user-led responses to misinformation on social media and outlines how they can be better supported via software tools. Findings show that users desire support tools designed to minimize time and effort in identifying misinformation and provide tailored suggestions for crafting responses to misinformation that account for emotional and relational context.

After his acquisition of Twitter, Elon Musk pledged to overhaul verification and moderation policies. These events sparked fears of a rise in influence of contentious actors—notably from the political right. I investigated whether these actors did receive increased engagement over this period by gathering tweet data for accounts that purchased blue-tick verification before and after the Musk takeover.

TikTok provides opportunity for citizen-led debunking where users correct other users’ misinformation. In the present study (N=1,169), participants either watched and rated the credibility of (1) a misinformation video, (2) a correction video, or (3) a misinformation video followed by a correction video (“debunking”).

Recent evidence suggests that prompting users to consider the accuracy of online posts increases the quality of news they share on social media. Here we examine how accuracy prompts affect user behavior in a more realistic context, and whether their effect can be enhanced by using colored borders to differentiate news from social content.

Facebook has claimed to fight misinformation notably by reducing the virality of posts shared by “repeat offender” websites. The platform recently extended this policy to groups. We identified websites and groups that repeatedly publish false information according to fact checkers and investigated the implementation and impact of Facebook’s measures against them.