Angry but Accurate: Detecting and Profiling the Counter-Misinformation Ecosystem on Twitter
For researchers studying misinformation dynamics, this challenges the assumption that negative emotion signals falsehood, showing instead that corrective posts are more negative.
The study analyzes 264,737 COVID-19 tweets to compare users who oppose vs. support misinformation, finding that counter-misinformation posts are more emotionally negative (anger, disgust, sadness) and come from more established accounts.
On social media, many users actively push back against false claims. Understanding who pushes back and how they do so matters, as this corrective activity is central to how misinformation is contested. We study this counter-misinformation ecosystem at scale: applying a domain-specific NLI model from our prior work to a large corpus of COVID-19 tweets, we classify 264,737 posts as supporting or opposing false claims and compare 23 user- and text-level features across the two groups. Contrary to the dominant assumption that negative emotion is a signature of falsehood, we find that anti-misinformation posts are more emotionally negative than pro-misinformation posts, with higher levels of anger, disgust, and sadness. These differences are modest in magnitude but consistent in direction across the negative emotions. We also find that posts opposing misinformation tend to come from more established users, i.e., older accounts, more followers, and higher listed counts.