# Detecting Synthetic Political Narratives in Cross-Platform Social Media Discourse

arXiv: https://arxiv.org/abs/2605.21540 · HTML: https://mlsanity.com/paper/2605.21540/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 20 May 2026.

## Builds on (most cited first, 10 of 10)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [From Deception to Detection: The Dual Roles of Large Language Models in Fake News](https://mlsanity.com/paper/2409.17416/) (2024)
- [Exposing Cross-Platform Coordinated Inauthentic Activity in the Run-Up to the 2024 U.S. Election](https://mlsanity.com/paper/2410.22716/) (2024)
- [Simulating Misinformation Propagation in Social Networks using Large Language Models](https://mlsanity.com/paper/2511.10384/) (2025)
- [Discovery and classification of Twitter bots](https://mlsanity.com/paper/2010.15393/) (2020)
- [Telegram as a Battlefield: Kremlin-related Communications during the Russia-Ukraine Conflict](https://mlsanity.com/paper/2501.01884/) (2025)
- [Russo-Ukrainian War: Prediction and explanation of Twitter suspension](https://mlsanity.com/paper/2306.03502/) (2023)
- [Studying Disinformation Narratives on Social Media with LLMs and Semantic Similarity](https://mlsanity.com/paper/2507.20066/) (2025)
- [Coordinated Information Dissemination on Telegram and Reddit During Political Turbulence: A Case Study of Venezuela in Global News Channels](https://mlsanity.com/paper/2602.13333/) (2026)
- [Cross-Platform Digital Discourse Analysis of Iran: Topics, Sentiment, Polarization, and Event Validation on Telegram and Reddit](https://mlsanity.com/paper/2603.11057/) (2026)

## Abstract (arXiv)

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.