The DOI matches its Crossref record; 40 of the 41 references it cites were found in a registry or an index.
Seeing Is Believing: Is Video Modality More Powerful in Spreading Fake News via Online Messaging Apps?
Open accessAbstract
Abstract False rumors on WhatsApp, the world’s largest messaging app, have led to mob lynching in India and other countries. Doctored videos sent over the platform have elicited visceral responses among users, resulting in the wrongful death of innocent people. Would the responses have been so strong if the false news were circulated in text or audio? Is video modality the reason for such powerful effects? We explored this question by comparing reactions to three false stories prepared in either text-only, audio-only, or video formats, among rural and urban users in India. Our findings reveal that video is processed more superficially, and therefore users believe in it more readily and share it with others. Aside from advancing our theoretical understanding of modality effects in the context of mobile media, our findings also hold practical implications for design of modality-based flagging of fake news, and literacy campaigns to inoculate users against misinformation.
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“The processing of videos that combine different modalities can result in cognitive overload, as many cognitive resources are devoted to encoding. This comes at the cost of systematically processing substantial arguments of the message (Sundar et al., 2021).”
From How persuasive are political cheapfakes disseminated via social media? The effects of out-of-context visual disinformation on message credibility and issue agreement (2025) · cites this paper as Sundar et al., 2021
In the citing paper
“The realism heuristic29,31 predicts that “people are more likely to trust audiovisual modality [relative to text] because its content has a higher resemblance to the real world.””
From Human detection of political speech deepfakes across transcripts, audio, and video (2024) · cites this paper as [29]
In the citing paper
“the emotional and motivational influences of visual communication have been attributed to why misleading viral videos have provoked mob-violence28,29.”
From Human detection of political speech deepfakes across transcripts, audio, and video (2024) · cites this paper as [29]
In the citing paper
“In an experiment using 3 false news videos as stimuli, researchers found that stories presented as videos are perceived as more credible than stories presented as text or read aloud in audio form29.”
From Human detection of political speech deepfakes across transcripts, audio, and video (2024) · cites this paper as [29]
Citations
Cites
Basics of Qualitative Research (3rd ed.): Techniques and Procedures for Developing Grounded Theory
Corbin, J., & Strauss, A. (2008) · SAGE · doi:10.4135/9781452230153 · Cited by 12,136
(2008) · Cited by 7,674
Confirmation Bias: A Ubiquitous Phenomenon in Many Guises
Nickerson, R. S. (1998) · Review of General Psychology · doi:10.1037/1089-2680.2.2.175 · Cited by 7,267
Book Review: The Psychology of Attitudes
Chakravarti, D., Eagly, A. H., & Chaiken, S. (1997) · Journal of Marketing Research · doi:10.2307/3151869 · Cited by 7,173
Chaiken, S. (1980) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.39.5.752 · Cited by 5,516
Bower, G. H. (1981) · American Psychologist · doi:10.1037/0003-066x.36.2.129 · Cited by 4,154
The limited capacity model of mediated message processing
Lang, A. (2000) · Journal of Communication · doi:10.1111/j.1460-2466.2000.tb02833.x · Cited by 2,153
Less than you think: Prevalence and predictors of fake news dissemination on Facebook
Guess, A., Nagler, J., & Tucker, J. (2019) · Science Advances · doi:10.1126/sciadv.aau4586 · Cited by 1,652
Social and Heuristic Approaches to Credibility Evaluation Online
Metzger, M. J., Flanagin, A. J., & Medders, R. B. (2010) · Journal of Communication · doi:10.1111/j.1460-2466.2010.01488.x · Cited by 1,394
Public health and online misinformation: challenges and recommendations
Swire-Thompson, B., & Lazer, D. (2020) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040119-094127 · Cited by 1,170
Flagged first, then by citations
1 of the 41 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Eagly, 1993, The psychology of attitudes
- Kumar, 2016, Comparison between rural and urban India: A critical analysis, International Journal of Current Research, 8, 43329
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Metadata
- Authors
- Sundar, S. S., Molina, M. D., & Cho, E.
- Affiliations
- Pennsylvania State University (US)
- Department of Advertising and Public Relations, Michigan State University, East Lansing, MI 48824, USA
- The College of New Jersey (US)
- Corresponding author
- Sundar, S Shyam
- Venue
- Journal of Computer-Mediated Communication
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-08-01
- Volume
- 26
- Issue
- 6
- Pages
- 301-319
- ISSN
- 1083-6101 (ISSN-L)
- OpenAlex
- W3188473680
- Cited by
- 212
- References
- 41
- Citations by year
- 2026: 49 · 2025: 62 · 2024: 42 · 2023: 38 · 2022: 19 · 2021: 2
- FWCI
- 34.99
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Journal of Computer-Mediated Communication (publishedVersion, cc-by)
- Funders
- University of Pennsylvania
- Pennsylvania State University
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Sociology and Political Science › Islamic Finance and Communication
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- fake news, misinformation, WhatsApp, messaging apps, belief in misinformation, urban-rural differences, misinformation literacy
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