愤怒但准确：检测和剖析推特上的反错误信息生态系统
Angry but Accurate: Detecting and Profiling the Counter-Misinformation Ecosystem on Twitter
- University of Southern California（南加州大学）
- Thomas Lord Department of Computer Science（托马斯·劳德计算机科学系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究推特反错误信息生态系统，用特定领域NLI模型分类新冠推文，对比支持或反对错误信息的两类推文的用户和文本特征，发现反错误信息推文负面情绪更强且发布者多为更成熟用户。
AI中文摘要：
在社交媒体上，许多用户积极反驳虚假声明。了解反驳者及其方式很重要，因为这种纠正活动对错误信息的抗争至关重要。我们大规模研究这种反错误信息生态系统：将先前工作中的特定领域NLI模型应用于大量新冠推文，对264,737条帖子进行分类，并比较两组的23个用户和文本级特征。与负面情绪是虚假信息标志的主流假设相反，我们发现反错误信息的帖子比支持错误信息的帖子在情绪上更负面，愤怒、厌恶和悲伤程度更高。这些差异在程度上较小，但在负面情绪方向上一致。我们还发现，反对错误信息的帖子往往来自更成熟的用户，即更老的账户、更多的关注者和更高的被列出次数。
英文摘要：
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 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.
