Detecting Synthetic Political Narratives in Cross-Platform Social Media Discourse

TL;DR

This paper introduces a cross-platform framework using four coordination signals to detect synthetic political narratives across social media, demonstrating its effectiveness on Telegram and Reddit data.

Contribution

The paper presents a novel multi-dimensional scoring framework for identifying synthetic political narratives, combining lexical, temporal, rhetorical, and semantic signals.

Findings

IntelSlava shows low lexical diversity and high burstiness, ranking highest in coordination score.

Rybar has high semantic homogenization but low lexical diversity and rhetorical overlap.

Multi-dimensional SNC(C) scoring outperforms individual metrics in detecting coordination.

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.