研究推特上反对假信息者的特征，发现他们更愤怒但更可信。
Angry but Accurate: Detecting and Profiling the Counter-Misinformation Ecosystem on Twitter
- 用特定领域文本蕴含模型分析26万条新冠推文，分类支持/反对假信息
- 反对假信息的推文情绪更负面，尤其愤怒、厌恶和悲伤感更强
- 反对者多为资深用户，账号更老、粉丝更多、被列在更多列表中
在社交媒体上，许多用户主动反驳虚假信息。理解这些反驳者及其行为至关重要，因为这是对抗错误信息的核心机制。本研究大规模分析了推特上的反假信息生态：利用我们前期开发的领域专用自然语言蕴含模型，对大量新冠相关推文进行分析，将264,737条推文分类为支持或反对虚假信息，并对比两组在23个用户与文本层面特征上的差异。与主流假设相反，我们发现反对假信息的推文情绪更负面，愤怒、厌恶和悲伤程度均高于支持者，虽差异幅度较小但方向一致。此外，反对假信息的推文多来自更成熟的用户，即账户年龄更长、关注者更多、被列入更多列表。
原文摘要 · Abstract (English)
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.
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