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

arXiv: https://arxiv.org/abs/2609.13671 · HTML: https://mlsanity.com/paper/2609.13671/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 12 Sep 2026.

## Builds on (most cited first, 12 of 12)

- [A Continuously Growing Dataset of Sentential Paraphrases](https://mlsanity.com/paper/1708.00391/) (2017)
- [Uncovering Coordinated Networks on Social Media: Methods and Case Studies](https://mlsanity.com/paper/2001.05658/) (2020)
- [Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the Web](https://mlsanity.com/paper/1801.09288/) (2018)
- [Unveiling Coordinated Groups Behind White Helmets Disinformation](https://mlsanity.com/paper/2003.01313/) (2020)
- [Tracking Fringe and Coordinated Activity on Twitter Leading Up To the US Capitol Attack](https://mlsanity.com/paper/2302.04450/) (2023)
- [To Normalize, or Not to Normalize: The Impact of Normalization on Part-of-Speech Tagging](https://mlsanity.com/paper/1707.05116/) (2017)
- [An Empirical Survey of Unsupervised Text Representation Methods on Twitter Data](https://mlsanity.com/paper/2012.03468/) (2020)
- [IOHunter: Graph Foundation Model to Uncover Online Information Operations](https://mlsanity.com/paper/2412.14663/) (2024)
- [Strategies and Vulnerabilities of Participants in Venezuelan Influence Operations](https://mlsanity.com/paper/2210.11673/) (2022)
- [The Language of Influence: Sentiment, Emotion, and Hate Speech in State Sponsored Influence Operations](https://mlsanity.com/paper/2505.07212/) (2025)
- [Toxicity in State Sponsored Information Operations](https://mlsanity.com/paper/2507.10936/) (2025)
- [Duplicating Deceit: Inauthentic Behavior Among Indian Misinformation Duplicators on X/Twitter](https://mlsanity.com/paper/2507.13636/) (2025)

## Abstract (arXiv)

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.