# Unmasking the Web of Deceit: Uncovering Coordinated Activity to Expose Information Operations on Twitter

arXiv: https://arxiv.org/abs/2310.09884 · HTML: https://mlsanity.com/paper/2310.09884/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2023.

## Built on by (most cited first, 13 of 13)

- [Leveraging Large Language Models to Detect Influence Campaigns in Social Media](https://mlsanity.com/paper/2311.07816/) (2023)
- [Detection and Characterization of Coordinated Online Behavior: A Survey](https://mlsanity.com/paper/2408.01257/) (2024)
- [Before It's Too Late: A State Space Model for the Early Prediction of Misinformation and Disinformation Engagement](https://mlsanity.com/paper/2502.04655/) (2025)
- [Emergent Coordinated Behaviors in Networked LLM Agents: Modeling the Strategic Dynamics of Information Operations](https://mlsanity.com/paper/2510.25003/) (2025)
- [IOHunter: Graph Foundation Model to Uncover Online Information Operations](https://mlsanity.com/paper/2412.14663/) (2024)
- [Large Language Models Reveal Information Operation Goals, Tactics, and Narrative Frames](https://mlsanity.com/paper/2405.03688/) (2024)
- [SoMeR: Multi-View User Representation Learning for Social Media](https://mlsanity.com/paper/2405.05275/) (2024)
- [Posts of Peril: Detecting Information About Hazards in Text](https://mlsanity.com/paper/2405.17838/) (2024)
- [Uncovering Social Network Activity Using Joint User and Topic Interaction](https://mlsanity.com/paper/2506.12842/) (2025)
- [Density-aware Walks for Coordinated Campaign Detection](https://mlsanity.com/paper/2506.13912/) (2025)
- [Multimodal Coordinated Online Behavior: Trade-offs and Strategies](https://mlsanity.com/paper/2507.12108/) (2025)
- [Beyond Content: Behavioral Policies Reveal Actors in Information Operations](https://mlsanity.com/paper/2602.02838/) (2026)
- [Predicting, Evaluating, and Explaining Top Misinformation Spreaders via Archetypal User Behavior](https://mlsanity.com/paper/2608.16323/) (2026)

## Abstract (arXiv)

Social media platforms, particularly Twitter, have become pivotal arenas for influence campaigns, often orchestrated by state-sponsored information operations (IOs). This paper delves into the detection of key players driving IOs by employing similarity graphs constructed from behavioral pattern data. We unveil that well-known, yet underutilized network properties can help accurately identify coordinated IO drivers. Drawing from a comprehensive dataset of 49 million tweets from six countries, which includes multiple verified IOs, our study reveals that traditional network filtering techniques do not consistently pinpoint IO drivers across campaigns. We first propose a framework based on node pruning that emerges superior, particularly when combining multiple behavioral indicators across different networks. Then, we introduce a supervised machine learning model that harnesses a vector representation of the fused similarity network. This model, which boasts a precision exceeding 0.95, adeptly classifies IO drivers on a global scale and reliably forecasts their temporal engagements. Our findings are crucial in the fight against deceptive influence campaigns on social media, helping us better understand and detect them.