Content-based features predict social media influence operations-Reference-Cited by-同舟云学术

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Content-based features predict social media influence operations

Published:2020-07-24 Issue:30 Volume:6 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Alizadeh Meysam¹^ORCID,Shapiro Jacob N.¹^ORCID,Buntain Cody²^ORCID,Tucker Joshua A.³^ORCID

Affiliation:

1. School of Public and International Affairs and Department of Politics, Princeton University, Princeton, NJ 08540, USA.

2. Department of Informatics, New Jersey Institute of Technology, Newark, NJ 07102, USA.

3. Department of Politics and Center for Social Media and Politics, New York University, New York, NY 10012, USA.

Abstract

Coordinated political influence operations leave a distinct signature in content that machine learning can detect.

Funder

Microsoft

Bertelsmann Foundation

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference41 articles.

1. Affective publics and structures of storytelling: sentiment, events and mediality

2. The battle for #Baltimore: Networked counterpublics and the contested framing of urban unrest;Welles B. F.;Int. J. Commun.,2019

3. D. A. Martin J. N. Shapiro Trends in Online Foreign Influence Efforts (Princeton Univ. 2019).

4. Political Astroturfing on Twitter: How to Coordinate a Disinformation Campaign

5. E. Walker Grassroots for Hire: Public Affairs Consultants in American Democracy (Cambridge Univ. Press 2014).

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