Automatic detection of influential actors in disinformation networks
Abstract
Hostile influence operations (IOs) that weaponize digital communications and social media pose a rising threat to open democracies. This paper presents a system framework to automate detection of disinformation narratives, networks, and influential actors. The framework integrates natural language processing, machine learning, graph analytics, and network causal inference to quantify the impact of individual actors in spreading the IO narrative. We present a classifier that detects reported IO accounts with 96% precision, 79% recall, and 96% AUPRC, demonstrated on real social media data collected for the 2017 French presidential election and known IO accounts disclosed by Twitter. Our system also discovers salient network communities and high-impact accounts that are independently corroborated by US Congressional reports and investigative journalism.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 07, 2021
- Source ID
- 10.1073/pnas.2011216118
Entities
People
- Danelle C. Shah
- Donald B. Rubin
- Edward K Kao
- Erika D Mackin
- Olga Simek
- Steven T Smith
Organizations
- Harvard University
- MIT Lincoln Laboratory
- Temple University
- Tsinghua University
- United States Department of Defense