This study explores potential indicators of coordinated influence among users active in both r/Sino and r/China—two ideologically opposed Reddit communities focused on Chinese political discourse. Using topic modeling and sentiment analysis, we construct a user–topic sentiment matrix to compare individual sentiment patterns against broader community baselines. We then apply behavioral profiling to full user histories, incorporating features such as account age, posting frequency, lexical diversity, and karma distribution. Users exhibiting multiple behavioral anomalies are further analyzed within a subreddit co-participation network to assess structural positioning and cross-community influence. Our integrated approach reveals patterns of sentiment deviation, rhetorical consistency, and anomalous engagement that suggest strategically structured or inauthentic behavior. These findings demonstrate the value of combining content and activity-based signals for detecting influence operations in open-source environments.

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Cross-Subreddit Behavior as Open-Source Indicators of Coordinated Influence: A Case Study of r/Sino & r/China

  • Manon Pilaud,
  • Ian McCulloh

摘要

This study explores potential indicators of coordinated influence among users active in both r/Sino and r/China—two ideologically opposed Reddit communities focused on Chinese political discourse. Using topic modeling and sentiment analysis, we construct a user–topic sentiment matrix to compare individual sentiment patterns against broader community baselines. We then apply behavioral profiling to full user histories, incorporating features such as account age, posting frequency, lexical diversity, and karma distribution. Users exhibiting multiple behavioral anomalies are further analyzed within a subreddit co-participation network to assess structural positioning and cross-community influence. Our integrated approach reveals patterns of sentiment deviation, rhetorical consistency, and anomalous engagement that suggest strategically structured or inauthentic behavior. These findings demonstrate the value of combining content and activity-based signals for detecting influence operations in open-source environments.