气候变化领域集成服务门户(CCPortal): Automatic detection of influential actors in disinformation networks

CCPortal

DOI	10.1073/PNAS.2011216118
	Automatic detection of influential actors in disinformation networks
	Smitha S.T.; Kaoa E.K.; MacKina E.D.; Shaha D.C.; Simeka O.; Rubin D.B.
发表日期	2021
ISSN	00278424
卷号	118 期号:4
英文摘要	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. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Causal inference; Influence operations; Machine learning; Networks; Social media
语种	英语
scopus关键词	article; disinformation; election; human; machine learning; narrative; natural language processing; publishing; quantitative analysis; recall; social media
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/180899
作者单位	Mit Lincoln Laboratory, Lexington, MA 02421, United States; Fox School of Business, Temple University, Philadelphia, PA 19122, United States; Yau Mathematical Sciences Center, Tsinghua University, Beijing, 100084, China; Department of Statistics, Harvard University, Cambridge, MA 02138, United States
推荐引用方式 GB/T 7714	Smitha S.T.,Kaoa E.K.,MacKina E.D.,et al. Automatic detection of influential actors in disinformation networks[J],2021,118(4).
APA	Smitha S.T.,Kaoa E.K.,MacKina E.D.,Shaha D.C.,Simeka O.,&Rubin D.B..(2021).Automatic detection of influential actors in disinformation networks.Proceedings of the National Academy of Sciences of the United States of America,118(4).
MLA	Smitha S.T.,et al."Automatic detection of influential actors in disinformation networks".Proceedings of the National Academy of Sciences of the United States of America 118.4(2021).