Quantifying the Causal Effects of Misinformation on Engagement and Emotions on Social Media

Misinformation drives engagement on social media platforms, yet the underlying emotional mechanisms remain poorly understood. Previous studies rely on biased samples and fail to distinguish between discrete emotions, while correlational approaches cannot disentangle the effects of misinformation from confounding factors such as political ideology. Whether misinformation causally affects online interactions therefore remains an open question. We analyze 9.9M German news tweets and 11M replies on Twitter (Oct 2020–Mar 2022) across news sources of varying trustworthiness. Using machine learning and causal inference methods, we examine whether news trustworthiness influences engagement and emotions in replies.Despite comprising only 6% of all news shared, untrustworthy sources receive 39% more retweets, 12% more quote retweets, but 14% fewer likes and 19% replies. Replies to untrustworthy content show 9% more anger, 22% more disgust, and 12% less joy, with anger potentially driven by self-selection. These findings show misinformation leaves distinct fingerprints online, with direct implications for platform design.