Detecting Synthetic Political Narratives in Cross-Platform Social Media Discourse

2026-05-20

AI Summary

This study addresses the challenge of detecting synthetic political narratives generated by large language models and coordinately disseminated across social media platforms. To this end, it proposes a multidimensional Synthetic Narrative Coordination (SNC) scoring framework that integrates four coordination signals: lexical diversity (measured by MATTR), temporal burstiness, rhetorical repetition (quantified via Jaccard overlap), and semantic homogenization. By combining these complementary indicators, the framework overcomes the limitations of single-metric approaches, enhancing both robustness and interpretability in detection. Experimental evaluation on a dataset of 350,000 cross-platform posts reveals that IntelSlava consistently achieves the highest SNC scores (ranging from 0.45 to 0.60) across most event windows, whereas Rybar exhibits weaker coordination signals due to linguistic heterogeneity. These findings underscore the necessity and efficacy of multidimensional assessment in identifying coordinated manipulation campaigns.

Abstract

The proliferation of large language models has introduced a new paradigm of synthetic political communication in which narratives may be generated, semantically coordinated, and strategically disseminated across platforms at scale. We present a cross-platform framework for detecting synthetic political narratives using four coordination signals -- lexical diversity D(C), temporal burstiness B(C), rhetorical repetition R(C), and semantic homogenization H(C) -- combined into a Synthetic Narrative Coordination Score SNC(C). We apply the framework to a corpus of 353,223 records spanning six geopolitical event windows collected from six Telegram channels and nine Reddit communities (2023--2026). Results show that IntelSlava exhibits the lowest lexical diversity (MATTR 0.52--0.54), the highest burstiness (B=+0.48 to +0.73), and the highest rhetorical overlap with peer channels (Jaccard 0.12), ranking first in the composite SNC(C) on four of six event windows (SNC 0.45--0.60). Rybar ranks last on all windows despite its high semantic homogenization, because its Russian-language output yields high lexical diversity and near-zero rhetorical Jaccard with English-language channels -- demonstrating that no single indicator is sufficient for coordination detection. Multi-dimensional SNC(C) scoring provides a more robust and interpretable signal than any individual metric.

Problem

• synthetic political narratives
• cross-platform detection
• social media coordination
• large language models
• narrative homogenization

Innovation

• synthetic political narratives
• cross-platform coordination
• coordination signals
• Synthetic Narrative Coordination Score
• large language models