Angry but Accurate: Detecting and Profiling the Counter-Misinformation Ecosystem on Twitter
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 misinformation-opposing posts are more emotionally negative than misinformation-supporting posts, with higher ...
| Authors | Eun Cheol Choi, Emilio Ferrara |
|---|---|
| Primary category | cs.SI |
| Published | 3 July 2026 |
| Last updated at source | 7 August 2026 |
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