Automatic detection of influential actors in disinformation networks
Copyright © 2021 the Author(s). Published by PNAS..
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.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2021 |
|---|---|
| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:118 |
|---|---|
| Enthalten in: |
Proceedings of the National Academy of Sciences of the United States of America - 118(2021), 4 vom: 26. Jan. |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Smith, Steven T [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Causal inference |
|---|
| Anmerkungen: |
Date Completed 11.06.2021 Date Revised 05.10.2022 published: Print Citation Status MEDLINE |
|---|
| doi: |
10.1073/pnas.2011216118 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM319779297 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM319779297 | ||
| 003 | DE-627 | ||
| 005 | 20231225172155.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1073/pnas.2011216118 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1065.xml |
| 035 | |a (DE-627)NLM319779297 | ||
| 035 | |a (NLM)33414276 | ||
| 035 | |a (PII)e2011216118 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Smith, Steven T |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Automatic detection of influential actors in disinformation networks |
| 264 | 1 | |c 2021 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 11.06.2021 | ||
| 500 | |a Date Revised 05.10.2022 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2021 the Author(s). Published by PNAS. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, U.S. Gov't, Non-P.H.S. | |
| 650 | 4 | |a causal inference | |
| 650 | 4 | |a influence operations | |
| 650 | 4 | |a machine learning | |
| 650 | 4 | |a networks | |
| 650 | 4 | |a social media | |
| 700 | 1 | |a Kao, Edward K |e verfasserin |4 aut | |
| 700 | 1 | |a Mackin, Erika D |e verfasserin |4 aut | |
| 700 | 1 | |a Shah, Danelle C |e verfasserin |4 aut | |
| 700 | 1 | |a Simek, Olga |e verfasserin |4 aut | |
| 700 | 1 | |a Rubin, Donald B |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Proceedings of the National Academy of Sciences of the United States of America |d 1915 |g 118(2021), 4 vom: 26. Jan. |w (DE-627)NLM000008982 |x 1091-6490 |7 nnns |
| 773 | 1 | 8 | |g volume:118 |g year:2021 |g number:4 |g day:26 |g month:01 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1073/pnas.2011216118 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 118 |j 2021 |e 4 |b 26 |c 01 | ||