Automatic Detection of Influential Actors in Disinformation Networks

TL;DR

This paper introduces an automated framework combining NLP, machine learning, graph analytics, and causal inference to identify influential actors in disinformation networks, demonstrated on real-world Twitter data from multiple campaigns.

Contribution

It presents a novel end-to-end system that accurately detects and ranks influential disinformation actors, surpassing traditional impact metrics and validated on extensive real-world datasets.

Findings

Detects IO accounts with 96% precision and 79% recall

Maps network communities and identifies high-impact actors

Validated against independent sources and known IO datasets

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.