# Uncovering Coordinated Networks on Social Media: Methods and Case Studies

arXiv: https://arxiv.org/abs/2001.05658 · HTML: https://mlsanity.com/paper/2001.05658/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2020.

## Built on by (most cited first, 30 of 49)

- [Factuality Challenges in the Era of Large Language Models](https://mlsanity.com/paper/2310.05189/) (2023)
- [BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs](https://mlsanity.com/paper/2206.10071/) (2022)
- [A Survey on Multimodal Disinformation Detection](https://mlsanity.com/paper/2103.12541/) (2021)
- [Anatomy of an AI-powered malicious social botnet](https://mlsanity.com/paper/2307.16336/) (2023)
- [How malicious AI swarms can threaten democracy: The fusion of agentic AI and LLMs marks a new frontier in information warfare](https://mlsanity.com/paper/2506.06299/) (2025)
- [VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media](https://mlsanity.com/paper/2110.15454/) (2021)
- [Characteristics and prevalence of fake social media profiles with AI-generated faces](https://mlsanity.com/paper/2401.02627/) (2024)
- [The Spread of Propaganda by Coordinated Communities on Social Media](https://mlsanity.com/paper/2109.13046/) (2021)
- [The Moltbook Illusion: Separating Human Influence from Emergent Behavior in AI Agent Societies](https://mlsanity.com/paper/2602.07432/) (2026)
- [COVID-19 Vaccine Misinformation Campaigns and Social Media Narratives](https://mlsanity.com/paper/2106.08423/) (2021)
- [Demystifying Misconceptions in Social Bots Research](https://mlsanity.com/paper/2303.17251/) (2023)
- [Temporal Dynamics of Coordinated Online Behavior: Stability, Archetypes, and Influence](https://mlsanity.com/paper/2301.06774/) (2023)
- [Emergent Coordinated Behaviors in Networked LLM Agents: Modeling the Strategic Dynamics of Information Operations](https://mlsanity.com/paper/2510.25003/) (2025)
- [Community Moderation and the New Epistemology of Fact Checking on Social Media](https://mlsanity.com/paper/2505.20067/) (2025)
- [Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump](https://mlsanity.com/paper/2204.12929/) (2022)
- [IOHunter: Graph Foundation Model to Uncover Online Information Operations](https://mlsanity.com/paper/2412.14663/) (2024)
- [Navigating the Post-API Dilemma | Search Engine Results Pages Present a Biased View of Social Media Data](https://mlsanity.com/paper/2401.15479/) (2024)
- [Coordinated Reply Attacks in Influence Operations: Characterization and Detection](https://mlsanity.com/paper/2410.19272/) (2024)
- [Analyzing the Strategy of Propaganda using Inverse Reinforcement Learning: Evidence from the 2022 Russian Invasion of Ukraine](https://mlsanity.com/paper/2307.12788/) (2023)
- [Unmasking Social Bots: How Confident Are We?](https://mlsanity.com/paper/2407.13929/) (2024)
- [AIMM: An AI-Driven Multimodal Framework for Detecting Social-Media-Influenced Stock Market Manipulation](https://mlsanity.com/paper/2512.16103/) (2025)
- [SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis](https://mlsanity.com/paper/2108.02572/) (2021)
- [VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search](https://mlsanity.com/paper/2108.02725/) (2021)
- [Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation](https://mlsanity.com/paper/2109.05778/) (2021)
- [Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors](https://mlsanity.com/paper/2202.02830/) (2022)
- [Automated clustering of COVID-19 anti-vaccine discourse on Twitter](https://mlsanity.com/paper/2203.01549/) (2022)
- [Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks](https://mlsanity.com/paper/2203.11011/) (2022)
- [Detection of Dangerous Events on Social Media: A Perspective Review](https://mlsanity.com/paper/2204.01351/) (2022)
- [Explanation-Guided Fairness Testing through Genetic Algorithm](https://mlsanity.com/paper/2205.08335/) (2022)
- [Using Graph Neural Networks for Program Termination](https://mlsanity.com/paper/2207.14648/) (2022)

## Abstract (arXiv)

Coordinated campaigns are used to influence and manipulate social media platforms and their users, a critical challenge to the free exchange of information online. Here we introduce a general, unsupervised network-based methodology to uncover groups of accounts that are likely coordinated. The proposed method constructs coordination networks based on arbitrary behavioral traces shared among accounts. We present five case studies of influence campaigns, four of which in the diverse contexts of U.S. elections, Hong Kong protests, the Syrian civil war, and cryptocurrency manipulation. In each of these cases, we detect networks of coordinated Twitter accounts by examining their identities, images, hashtag sequences, retweets, or temporal patterns. The proposed approach proves to be broadly applicable to uncover different kinds of coordination across information warfare scenarios.