Not All Duplicates Are Coordination: Generic vs. Non-Generic Duplicate Campaigns in Information Operations

2026-09-11

AI Summary

研究通过区分通用与非通用重复内容，使用LLM辅助标注和监督分类器处理187,000条推文，以提高信息操作中基于重复内容的协调行为分析准确性。

Abstract

Duplicate content is widely used to study coordinated behavior in social media information operations (IOs), but not all repetition provides equally meaningful evidence of coordination. Generic, reusable, or low-information posts may create noisy account-account links when projected into coordination graphs. We study this problem using 187,000 English-language tweets from six Russian Twitter Information Operations datasets. We introduce a generic/non-generic distinction for duplicate campaigns, label tweets using an LLM-assisted protocol with independent human validation, and train supervised classifiers over sentence embeddings to scale the labels. We construct duplicate campaigns using lexical similarity and two embedding-based methods. Generic campaigns are rare under lexical matching but account for nearly 39% of campaigns detected by embedding-based methods. Restricting graphs to non-generic campaigns reduces graph size and the largest connected component while increasing density, suggesting a smaller but more focused coordination structure. These findings show that duplicate-based coordination analysis should consider both textual similarity and semantic specificity.

Problem

• duplicate content
• coordinated behavior
• social media information operations
• generic posts
• noisy account-account links

Innovation

• duplicate campaigns
• generic vs. non-generic
• LLM-assisted protocol
• sentence embeddings
• coordination structure