# Automatic Detection of Influential Actors in Disinformation Networks

arXiv: https://arxiv.org/abs/2005.10879 · HTML: https://mlsanity.com/paper/2005.10879/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2020.

## Built on by (most cited first, 8 of 8)

- [Temporal Dynamics of Coordinated Online Behavior: Stability, Archetypes, and Influence](https://mlsanity.com/paper/2301.06774/) (2023)
- [A Monte Carlo Language Model Pipeline for Zero-Shot Sociopolitical Event Extraction](https://mlsanity.com/paper/2305.15051/) (2023)
- [Inductive detection of Influence Operations via Graph Learning](https://mlsanity.com/paper/2305.16544/) (2023)
- [Causal Influences over Social Learning Networks](https://mlsanity.com/paper/2307.09575/) (2023)
- [Exposing the Obscured Influence of State-Controlled Media: A Causal Estimation of Influence Between Media Outlets Via Quotation Propagation](https://mlsanity.com/paper/2201.05985/) (2022)
- [ChestyBot: Detecting and Disrupting Chinese Communist Party Influence Stratagems](https://mlsanity.com/paper/2505.10746/) (2025)
- [Unmasking Superspreaders: Data-Driven Approaches for Identifying and Comparing Key Influencers of Conspiracy Theories on X.com](https://mlsanity.com/paper/2602.04546/) (2026)
- [Unsupervised Anomaly Detection of Information Operations Users via Behavioral and Language Patterns](https://mlsanity.com/paper/2607.05855/) (2026)

## Abstract (arXiv)

The weaponization of digital communications and social media to conduct disinformation campaigns at immense scale, speed, and reach presents new challenges to identify and counter hostile influence operations (IOs). This paper presents an end-to-end framework to automate detection of disinformation narratives, networks, and influential actors. The framework integrates natural language processing, machine learning, graph analytics, and a novel network causal inference approach to quantify the impact of individual actors in spreading IO narratives. We demonstrate its capability on real-world hostile IO campaigns with Twitter datasets collected during the 2017 French presidential elections, and known IO accounts disclosed by Twitter over a broad range of IO campaigns (May 2007 to February 2020), over 50,000 accounts, 17 countries, and different account types including both trolls and bots. Our system detects IO accounts with 96% precision, 79% recall, and 96% area-under-the-PR-curve, maps out salient network communities, and discovers high-impact accounts that escape the lens of traditional impact statistics based on activity counts and network centrality. Results are corroborated with independent sources of known IO accounts from U.S. Congressional reports, investigative journalism, and IO datasets provided by Twitter.