用社交网络与话题分析，揭示疫情谣言传播中用户行为与内容的关联。
DISHONEST: Dissecting misInformation Spread using Homogeneous sOcial NEtworks and Semantic Topic classification
- 结合推文转发网络与话题建模，量化用户社交互动与内容同质性。
- 发现用户社交活跃度与内容单一性存在显著正相关。
- 适合关注社交媒体谣言机制、网络社群行为的研究者阅读。
新冠疫情爆发后，推特等在线平台上的虚假信息传播显著增加，常被归因于“回音室”效应。这种现象体现在两个维度：一是用户社交互动中倾向于固守相似群体；二是其发布内容反复传播相同观点。本研究通过推特的转发网络分析社交互动，并利用主题建模分析推文内容，提出一种新指标来衡量用户在社交网络中移动的速度，以评估其互动多样性。对疫情相关虚假信息数据的应用显示，用户社交行为与内容同质性之间存在显著相关性。这一结果支持了关于反社会用户行为的普遍直觉，表明即使在已充斥虚假信息的子社群中，该模式依然成立。
原文摘要 · Abstract (English)
The emergence of the COVID-19 pandemic resulted in a significant rise in the spread of misinformation on online platforms such as Twitter. Oftentimes this growth is blamed on the idea of the "echo chamber." However, the behavior said to characterize these echo chambers exists in two dimensions. The first is in a user's social interactions, where they are said to stick with the same clique of like-minded users. The second is in the content of their posts, where they are said to repeatedly espouse homogeneous ideas. In this study, we link the two by using Twitter's network of retweets to study social interactions and topic modeling to study tweet content. In order to measure the diversity of a user's interactions over time, we develop a novel metric to track the speed at which they travel through the social network. The application of these analysis methods to misinformation-focused data from the pandemic demonstrates correlation between social behavior and tweet content. We believe this correlation supports the common intuition about how antisocial users behave, and further suggests that it holds even in subcommunities already rife with misinformation.
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