# A General Method to Find Highly Coordinating Communities in Social Media through Inferred Interaction Links

arXiv: https://arxiv.org/abs/2103.03409 · HTML: https://mlsanity.com/paper/2103.03409/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2021.

## Built on by (most cited first, 10 of 10)

- [Leveraging Large Language Models to Detect Influence Campaigns in Social Media](https://mlsanity.com/paper/2311.07816/) (2023)
- [The Spread of Propaganda by Coordinated Communities on Social Media](https://mlsanity.com/paper/2109.13046/) (2021)
- [Temporal Dynamics of Coordinated Online Behavior: Stability, Archetypes, and Influence](https://mlsanity.com/paper/2301.06774/) (2023)
- [Detection and Characterization of Coordinated Online Behavior: A Survey](https://mlsanity.com/paper/2408.01257/) (2024)
- ["We Demand Justice!": Towards Social Context Grounding of Political Texts](https://mlsanity.com/paper/2311.09106/) (2023)
- [Mobile Edge Computing, Metaverse, 6G Wireless Communications, Artificial Intelligence, and Blockchain: Survey and Their Convergence](https://mlsanity.com/paper/2209.14147/) (2022)
- [Developing a Trusted Human-AI Network for Humanitarian Benefit](https://mlsanity.com/paper/2112.11191/) (2021)
- [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)
- [Adaptive Causal Coordination Detection for Social Media: A Memory-Guided Framework with Semi-Supervised Learning](https://mlsanity.com/paper/2601.00400/) (2026)

## Abstract (arXiv)

Political misinformation, astroturfing and organised trolling are online malicious behaviours with significant real-world effects. Many previous approaches examining these phenomena have focused on broad campaigns rather than the small groups responsible for instigating or sustaining them. To reveal latent (i.e., hidden) networks of cooperating accounts, we propose a novel temporal window approach that relies on account interactions and metadata alone. It detects groups of accounts engaging in various behaviours that, in concert, come to execute different goal-based strategies, a number of which we describe. The approach relies upon a pipeline that extracts relevant elements from social media posts, infers connections between accounts based on criteria matching the coordination strategies to build an undirected weighted network of accounts, which is then mined for communities exhibiting high levels of evidence of coordination using a novel community extraction method. We address the temporal aspect of the data by using a windowing mechanism, which may be suitable for near real-time application. We further highlight consistent coordination with a sliding frame across multiple windows and application of a decay factor. Our approach is compared with other recent similar processing approaches and community detection methods and is validated against two relevant datasets with ground truth data, using content, temporal, and network analyses, as well as with the design, training and application of three one-class classifiers built using the ground truth; its utility is furthermore demonstrated in two case studies of contentious online discussions.