  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.
