Beyond Text: Multimodal Credibility Assessment Approaches for Online User-Generated Content
Abstract
User-generated content (UGC) is increasingly becoming prevalent on various digital platforms. The content generated on social media, review forums, and question–answer platforms impacts a larger audience and influences their political, social, and other cognitive abilities. Traditional credibility assessment mechanisms involve assessing the credibility of the source and the text. However, with the increase in how user content can be generated and shared (audio, video, and images), multimodal representation of UGC has become increasingly popular. This article reviews the credibility assessment of UGC in various domains, particularly identifying fake news, suspicious profiles, and fake reviews and testimonials, focusing on both textual content and the source of the content creator. Next, the concept of multimodal credibility assessment is presented, which also includes audio, video, and images in addition to text. After that, the article presents a systematic review and comprehensive analysis of work done in the credibility assessment of UGC considering multimodal features. Additionally, the article provides extensive details on the publicly available multimodal datasets for the credibility assessment of UGC. In the end, the research gaps, challenges, and future directions in assessing the credibility of multimodal UGC are presented.
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Misinformation, bias, and broadcasting of unconfirmed and altered content are dangers to public debate and democratic processes that rely on an informed citizenry.
“Misinformation, bias, and the broadcasting of unconfirmed and altered content are enormous dangers to public debate and the democratic processes that rely on an informed citizenry (Hochstein et al., 2023; Choudhary et al., 2025).”
From Effectiveness of citizen journalism: evaluating the credibility, bias, and impact of user-generated news content in the digital age (2025) · cites this paper as Choudhary et al., 2025
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Flagged first, then by citations
Cited by
Sorted by citations
Zhou, S. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-06216-6 · Cited by 3 · see citing works
- Authors
- Choudhary, M., Chouhan, S. S., & Rathore, S. S.
- Affiliations
- Malaviya National Institute of Technology Jaipur (IN)
- Atal Bihari Vajpayee Indian Institute of Information Technology and Management (IN)
- Venue
- ACM Transactions on Intelligent Systems and Technology
- Published
- 2024-10-31
- Volume
- 15
- Issue
- 5
- Pages
- 1-33
- ISSN
- 2157-6904 (ISSN-L)
- 2157-6912
- OpenAlex
- W4399671515
- Cited by
- 20
- References
- 182
- Citations by year
- 2026: 11 · 2025: 9
- FWCI
- 14.41
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Intelligent Systems and Technology (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- user-generated content, fake news detection, fake reviews, testimonials, video, images, multimodal dataset, source credibility
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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