通过多维度信号识别跨平台伪造政治叙事，发现特定账号存在协同传播特征。
Detecting Synthetic Political Narratives in Cross-Platform Social Media Discourse
- 构建合成叙事协调度评分SNC(C)，融合词汇多样性、时间爆发性等四类信号。
- 检测出IntelSlava在多个事件中协调度最高，而其他账号虽同质化却未协同。
- 适用于监测虚假信息传播，尤其适合关注社交媒体操纵的政策研究者。
大规模语言模型的兴起催生了可跨平台规模化生成、语义协调与策略传播的合成政治叙事。本文提出一种跨平台检测框架，基于四类协调信号——词汇多样性D(C)、时间爆发性B(C)、修辞重复率R(C)和语义同质化H(C)——构建合成叙事协调度评分SNC(C)。将该框架应用于2023—2026年间从六个Telegram频道和九个Reddit社区收集的353,223条记录，涵盖六个地缘政治事件窗口。结果表明，IntelSlava表现出最低的词汇多样性（MATTR 0.52–0.54）、最高的爆发性（B=+0.48至+0.73）以及与其他频道最高的修辞重叠（Jaccard 0.12），在六个事件窗口中有四个排名第一（SNC 0.45–0.60）。Rybar尽管语义同质化高，但在所有窗口排名最低，因其俄语输出带来高词汇多样性且与英文频道修辞重叠近乎为零，说明单一指标无法有效识别协同行为。多维SNC(C)评分比任一单一指标更具鲁棒性和可解释性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。