An empirical approach to understanding users' fake news identification on social media
Abstract
Purpose: During the past few years, the rise in social media use for information purposes in the absence of adequate control mechanisms has led to growing concerns about the reliability of the information in circulation and increased the presence of fake news. While this topic has recently gained researchers' attention, very little is known about users' fake news identification behavior. Hence, the purpose of this study is to understand the factors that contribute to individuals' identification of fake news on social media. Design/methodology/approach: This study employs a quantitative approach and proposes a behavioral model that explores the factors influencing users' identification of fake news on social media. It relies on data collected from a sample of 211 social media users which is tested using SEM. Findings: The findings show that expertise in social media use and verification behavior have a positive impact on fake news identification, while trust in social media as an information channel decreases this identification behavior. Furthermore, results establish the mediating role of social media information trust and verification behavior. Originality/value: The present study enhances our understanding of social media users' fake news identification by presenting a behavioral model. It is one of the few that focuses on the individual and argues that by identifying the factors that reinforce users' fake news identification behavior on social media, this type of misinformation can be reduced. It offers several theoretical and practical contributions.
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Social credibility increases individuals' trust in information found on social media.
“According to a few studies in our corpus, this is because social credibility increases individuals' trust in information found on social media [4,109].”
From What Do We Mean When We Talk about Trust in Social Media? A Systematic Review (2023) · cites this paper as [4]
Verification behaviors (checking completeness, searching for additional sources, considering biases, investigating currency) are unaffected by users' trust in information.
“Verifcation behaviors include activities such as checking information completeness and comprehensiveness, searching for additional sources to validate the information, considering the biases/agenda of the original authors, and investigating if the information was current and up-to-date, were all unafected by users' trust in information [4].”
From What Do We Mean When We Talk about Trust in Social Media? A Systematic Review (2023) · cites this paper as [4]
Trust in information on social media does not affect user verification behaviors, but it decreases users' probability of verifying fake news on social media.
“One study found that trust in information on social media does not afect user verifcation behaviors, though this trust decreases users' probability of verifying fake news on social media [4].”
From What Do We Mean When We Talk about Trust in Social Media? A Systematic Review (2023) · cites this paper as [4]
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When algorithms speak: Boundary conditions of information adoption in AI- and user-generated content
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Sustainable Education and Social Networks
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Analysis of the Impact of Social Media on Cryptocurrency Price Predictions
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A Context Aware Approach for Fake News Detection
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Statistical Analysis on Impact of Fake News in Social Media
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Observed Correction Increases Accuracy
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The Theory of Observed Correction
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- Authors
- Aoun Barakat, K., Dabbous, A., & Tarhini, A.
- Affiliations
- Saint Joseph University (LB)
- Lebanese American University (LB)
- Venue
- Online Information Review
- Publisher
- Emerald
- Published
- 2021-03-19
- Volume
- 45
- Issue
- 6
- Pages
- 1080-1096
- ISSN
- 1468-4527 (ISSN-L)
- OpenAlex
- W3138223205
- Cited by
- 80
- References
- 63
- Citations by year
- 2026: 5 · 2025: 33 · 2024: 11 · 2023: 17 · 2022: 13 · 2021: 1
- FWCI
- 14.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Online Information Review (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Islamic Finance and Communication
- Keywords
- misinformation, social media users, verification behavior, trust in social media, trust in information, behavioral models
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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