# On the Detection of Disinformation Campaign Activity with Network Analysis

arXiv: https://arxiv.org/abs/2005.13466 · HTML: https://mlsanity.com/paper/2005.13466/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2020.

## Built on by (most cited first, 15 of 15)

- [Coordinating Narratives and the Capitol Riots on Parler](https://mlsanity.com/paper/2109.00945/) (2021)
- [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)
- [Emergent Coordinated Behaviors in Networked LLM Agents: Modeling the Strategic Dynamics of Information Operations](https://mlsanity.com/paper/2510.25003/) (2025)
- [Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation](https://mlsanity.com/paper/2506.07211/) (2025)
- [False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulations](https://mlsanity.com/paper/2308.12497/) (2023)
- [QCRI's COVID-19 Disinformation Detector: A System to Fight the COVID-19 Infodemic in Social Media](https://mlsanity.com/paper/2204.03506/) (2022)
- [Inductive detection of Influence Operations via Graph Learning](https://mlsanity.com/paper/2305.16544/) (2023)
- [Analyzing the Strategy of Propaganda using Inverse Reinforcement Learning: Evidence from the 2022 Russian Invasion of Ukraine](https://mlsanity.com/paper/2307.12788/) (2023)
- [User Experience Design for Automatic Credibility Assessment of News Content About COVID-19](https://mlsanity.com/paper/2204.13943/) (2022)
- [ChestyBot: Detecting and Disrupting Chinese Communist Party Influence Stratagems](https://mlsanity.com/paper/2505.10746/) (2025)
- [Multimodal Coordinated Online Behavior: Trade-offs and Strategies](https://mlsanity.com/paper/2507.12108/) (2025)
- [Unsupervised Anomaly Detection of Information Operations Users via Behavioral and Language Patterns](https://mlsanity.com/paper/2607.05855/) (2026)
- [The Limits of Query Monitoring against Distributed Model Extraction](https://mlsanity.com/paper/2610.13083/) (2026)

## Abstract (arXiv)

Online manipulation of information has become more prevalent in recent years as state-sponsored disinformation campaigns seek to influence and polarize political topics through massive coordinated efforts. In the process, these efforts leave behind artifacts, which researchers have leveraged to analyze the tactics employed by disinformation campaigns after they are taken down. Coordination network analysis has proven helpful for learning about how disinformation campaigns operate; however, the usefulness of these forensic tools as a detection mechanism is still an open question. In this paper, we explore the use of coordination network analysis to generate features for distinguishing the activity of a disinformation campaign from legitimate Twitter activity. Doing so would provide more evidence to human analysts as they consider takedowns. We create a time series of daily coordination networks for both Twitter disinformation campaigns and legitimate Twitter communities, and train a binary classifier based on statistical features extracted from these networks. Our results show that the classifier can predict future coordinated activity of known disinformation campaigns with high accuracy (F1 = 0.98). On the more challenging task of out-of-distribution activity classification, the performance drops yet is still promising (F1 = 0.71), mainly due to an increase in the false positive rate. By doing this analysis, we show that while coordination patterns could be useful for providing evidence of disinformation activity, further investigation is needed to improve upon this method before deployment at scale.