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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In the citing paper
“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]
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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