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

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

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.

Social and Information Networks (cs.SI); Machine Learning (cs.LG)

arXiv:2609.13671 [cs.SI]

(or arXiv:2609.13671v1 [cs.SI] for this version)

https://doi.org/10.48550/arXiv.2609.13671 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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