Retracted research discussed on social media can spread misinformation, yet we lack an understanding of how retracted articles are mentioned by academic and non-academic users. This is especially relevant on Twitter due to the platform's prominent role in science communication. Here, we analyze the pre and post retraction differences in Twitter engagement metrics and content of mentions for over 3,800 retracted English-language articles alongside comparable non-retracted articles. We subset these findings according to the five user types detected by our supervised learning classifier: members of the public, scientists, bots, practitioners, and science communicators. We find that retracted articles receive greater overall engagement than non-retracted articles, especially among members of the public and bot users, the majority of engagement happening prior to retraction. Our results highlight non-scientists' involvement in retracted article discussions and suggest an opportunity for Twitter to include a retraction notice feature.

Drawing upon theories of risk and decision making, we present a theoretical framework for how the emotional attributes of social media content influence risk behaviors. We apply our framework to understanding how COVID-19 vaccination Twitter posts influence acceptance of the vaccine in Peru, the country with the highest relative number of COVID-19 excess deaths. By employing computational methods, topic modeling, and vector autoregressive time series analysis, we show that the prominence of expressed emotions about COVID-19 vaccination in social media content is associated with the daily percentage of Peruvian social media survey respondents who are vaccine-accepting over 231 days. Our findings show that net (positive) sentiment and trust emotions expressed in tweets about COVID-19 are positively associated with vaccine acceptance among survey respondents one day after the post occurs. This study demonstrates that the emotional attributes of social media content, besides veracity or informational attributes, may influence vaccine acceptance for better or worse based on its valence.