Automatic detection of influential actors in disinformation networks
| Autor: | Steven T. Smith, Donald B. Rubin, Erika Mackin, Edward K. Kao, Olga Simek, Danelle C. Shah |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science social media Machine Learning (stat.ML) 02 engineering and technology Statistics - Applications Machine Learning (cs.LG) Social Networking Statistics - Machine Learning 020204 information systems 050602 political science & public administration 0202 electrical engineering electronic engineering information engineering Humans Social media Narrative Applications (stat.AP) causal inference influence operations Social and Information Networks (cs.SI) Multidisciplinary Presidential system Information Dissemination Computer Sciences Communication Communications Media 05 social sciences Politics Computer Science - Social and Information Networks Data science 0506 political science machine learning Salient Causal inference Scale (social sciences) networks Physical Sciences Disinformation Centrality Social Network Analysis |
| Zdroj: | Proceedings of the National Academy of Sciences of the United States of America |
| ISSN: | 1091-6490 |
| Popis: | Significance 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. 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 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 precision-recall (P-R) 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 US Congressional reports, investigative journalism, and IO datasets provided by Twitter. |
| Databáze: | OpenAIRE |
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