Visual misinformation on Facebook

Yang, Yunkang; Davis, Trevor; Hindman, Matthew

doi:10.1093/joc/jqac051

Cited by 101Misinformation and Its ImpactsSocial Media and PoliticsSpam and Phishing Detection

Visual misinformation on Facebook

Yang, Yunkang, Davis, Trevor, Hindman, Matthew·2023·Journal of Communication

DOI 10.1093/joc/jqac051

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Abstract

Abstract We conduct the first large-scale study of image-based political misinformation on Facebook. We collect 13,723,654 posts from 14,532 pages and 11,454 public groups from August through October 2020, posts that together account for nearly all engagement of U.S. public political content on Facebook. We use perceptual hashing to identify duplicate images and computer vision to identify political figures. Twenty-three percent of sampled political images (N = 1,000) contained misinformation, as did 20% of sampled images (N = 1,000) containing political figures. We find enormous partisan asymmetry in misinformation posts, with right-leaning images 5–8 times more likely to be misleading, but little evidence that misleading images generate higher engagement. Previous scholarship, which mostly cataloged links to noncredible domains, has ignored image posts which account for a higher volume of misinformation. This research shows that new computer-assisted methods can scale to millions of images, and help address perennial and long-unanswered calls for more systematic study of visual political communication.

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Manipulated political images already make up a substantial portion (~20%) of visual misinformation on social media.
“Manipulated political images already make up a substantial portion (∼20%) of visual misinformation on social media (16).”
From The impact of generative artificial intelligence on socioeconomic inequalities and policy making (2024) · cites this paper as [16]

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Authors: Yang, Y., Davis, T., & Hindman, M.
Affiliations: Texas A&M University (US)
Columbia University (US)
George Washington University (US)
Corresponding author: Hindman, Matthew
ORCID: Yang, Yunkang
Hindman, Matthew
Venue: Journal of Communication
Publisher: Oxford University Press (OUP)
Published: 2023-02-28
Volume: 73
Issue: 4
Pages: 316-328
ISSN: 0021-9916 (ISSN-L)
1460-2466
DOI: 10.1093/joc/jqac051
OpenAlex: W4322732716
Cited by: 101
References: 60
Citations by year: 2026: 26 · 2025: 39 · 2024: 23 · 2023: 11 · 2022: 2
FWCI: 43.15
Type: journal article
Language: en
Open access: closed
Licence: academic.oup.com/pages/standard-publication-reuse-rights (vor)
Locations: Journal of Communication (publishedVersion)
Funders: John S. and James L. Knight Foundation
Institute for Data, Democracy & Politics at the George Washington University
Topics: Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
Social Sciences › Communication › Social Media and Politics
Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
Keywords: visual misinformation, political misinformation, Facebook, political communication, perceptual hashing, partisan asymmetry, engagement, 2020 elections

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Visual misinformation on Facebook

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