Duplicate content reveals focused coordination in information campaigns
Not All Duplicates Are Coordination: Generic vs. Non-Generic Duplicate Campaigns in Information Operations
Social and Information NetworksMachine Learning
Summary
Not all repeated social media posts mean that accounts are working closely together. The authors studied many Russian tweets to tell apart generic repeated posts from more meaningful duplicates that show real coordination. They used artificial intelligence and human checking to label these posts and trained a machine to spot them on a large scale. When ignoring generic repeats, they found smaller groups of accounts likely working together more directly. This helps improve how experts spot coordinated social media campaigns.
What this means in practice
- •For social media monitoring teams: Identify and focus coordination detection on specific, non-generic duplicate posts to reduce noise in campaign graphs.
- •For content moderation teams: Filter out generic repeated content when assessing coordinated misinformation efforts to prioritize impactful interventions.
Authors
Ashfaq Ali Shafin, Khandaker Mamun Ahmed
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.