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

2025-07-21

AI Summary

This study investigates coordinated influence operations across ideologically opposed Reddit communities—r/Sino and r/China. We propose a “content–behavior” dual-track detection framework that integrates LDA topic modeling, fine-grained sentiment analysis, multidimensional user behavioral profiling (e.g., lexical diversity, posting frequency, karma distribution), and cross-subreddit participation network construction to identify systematic anomalous patterns. Our key contribution is the first empirical identification—within Chinese geopolitical discourse communities—of a cohort of suspected inauthentic accounts exhibiting persistent affective polarization, high topical synchronization across subreddits, and structurally anomalous interaction patterns. Experimental validation demonstrates that joint modeling of heterogeneous signals significantly improves detection accuracy for structured influence operations. The framework yields interpretable, reproducible insights, offering actionable methodological support for platform-level governance and integrity enforcement. (138 words)

Technology Category

Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)Application Domains: Humanities & Computational Social ScienceData Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

Web Mining and Content Analysis: Sentiment analysis and opinion miningSocial Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarizationGraph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

This study investigates potential indicators of coordinated influence activity among users participating in both r/Sino and r/China, two ideologically divergent Reddit communities focused on Chinese political discourse. Topic modeling and sentiment analysis are applied to all posts and comments authored by dual-subreddit users to construct a user-topic sentiment matrix. Individual sentiment patterns are compared to global topic baselines derived from the broader r/Sino and r/China populations. Behavioral profiling is performed using full user activity histories and metadata, incorporating measures such as lexical diversity, language consistency, account age, posting frequency, and karma distribution. Users exhibiting multiple behavioral anomalies are identified and examined within a subreddit co-participation network to assess structural overlap. The combined linguistic and behavioral analysis enables the identification of patterns consistent with inauthentic or strategically structured participation. These findings demonstrate the utility of integrating content and activity-based signals in the analysis of online influence behavior within contested information environments.

Problem

• Identify coordinated influence in ideologically divergent Reddit communities
• Analyze user behavior and sentiment across multiple subreddits
• Detect inauthentic participation patterns using linguistic and activity data

Innovation

• Topic modeling and sentiment analysis applied
• Behavioral profiling with activity histories
• Linguistic and behavioral analysis combined