Angry but Accurate: Detecting and Profiling the Counter-Misinformation Ecosystem on Twitter

2026-07-02

AI Summary

This study investigates user behaviors and correction mechanisms related to countering misinformation on social media. Leveraging a dataset of 264,737 COVID-19–related tweets, the research employs a domain-specific natural language inference (NLI) model to distinguish posts that support versus those that oppose misinformation, followed by a systematic comparison of textual and account-level characteristics between the two groups. The findings reveal that while content opposing misinformation tends to express more negative emotions—such as anger and disgust—its authors are predominantly experienced and credible users, thereby challenging the prevailing assumption that negative sentiment serves as a reliable indicator of misinformation. This work contributes both empirical insights and methodological innovation to the understanding of online correction ecosystems.

Abstract

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.

Problem

• counter-misinformation
• social media
• misinformation
• emotional expression
• user profiling

Innovation

• counter-misinformation
• natural language inference (NLI)
• emotion analysis
• social media ecosystem
• misinformation detection