Automatic Detection of Influential Actors in Disinformation Networks
arXiv:2005.10879 · doi:10.1073/pnas.2011216118
Abstract
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.
arXiv commentProc. Natl. Acad. Sciences U.S.A. Vol. 118, No. 4, e2011216118
References in corpus (20)
- Scikit-learn: Machine Learning in Python
- Homophily and Contagion Are Generically Confounded in Observational Social Network Studies
- Snorkel: Rapid Training Data Creation with Weak Supervision
- BotOrNot: A System to Evaluate Social Bots
- Bots increase exposure to negative and inflammatory content in online social systems
- Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election
- Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models
- Online Human-Bot Interactions: Detection, Estimation, and Characterization
- Graph cluster randomization: network exposure to multiple universes
- Elements of estimation theory for causal effects in the presence of network interference
- Still out there: Modeling and Identifying Russian Troll Accounts on Twitter
- Detecting Troll Behavior via Inverse Reinforcement Learning: A Case Study of Russian Trolls in the 2016 US Election
- Network Detection Theory and Performance
- Bayesian Discovery of Threat Networks
- Influence Estimation on Social Media Networks Using Causal Inference
- Random Graph Asymptotics for Treatment Effect Estimation under Network Interference
- Contrast Specific Propensity Scores
- Causal Inference Under Network Interference: A Framework for Experiments on Social Networks
- Causal Diagrams for Interference
- Analyzing two-stage experiments in the presence of interference