Fake images: The effects of source, intermediary, and digital media literacy on contextual assessment of image credibility online

By Cuihua Shen, M. Kasra, Wenjing Pan, Grace A. Bassett, Y. Malloch, James F. O'Brien. 2018

174 citations recorded by the source corpus.

Fake or manipulated images propagated through the Web and social media have the capacity to deceive, emotionally distress, and influence public opinions and actions. Yet few studies have examined how individuals evaluate the authenticity of images that accompany online stories. This article details a 6-batch large-scale online experiment using Amazon Mechanical Turk that probes how people evaluate image credibility across online platforms. In each batch, participants were randomly assigned to 1 of 28 news-source mockups featuring a forged image, and they evaluated the credibility of the images based on several features. We found that participants’ Internet skills, photo-editing experience, and social media use were significant predictors of image credibility evaluation, while most social and heuristic cues of online credibility (e.g. source trustworthiness, bandwagon, intermediary trustworthiness) had no significant impact. Viewers’ attitude toward a depicted issue also positively influenced their credibility evaluation.

Subfields: Artificial Intelligence, Communication, Sociology and Political Science, AI Ethics, Fairness & Society, Social Sciences & Humanities

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Citations in the corpus

Political Ideology Predicts Perceptions of the Threat of COVID-19 (and Susceptibility to Fake News About It) (2020, background): “Mechanical Turk workers may have greater internet skills (Shen et al., 2019) that could make them less susceptible to fake news.”
Credibility Perceptions and Detection Accuracy of Fake News Headlines on Social Media: Effects of Truth-Bias and Endorsement Cues (2020, methodology): “…paradigm, prevents assessments of perceived authenticity and believability (Appelman & Sundar, 2016), and (2) we did not collect data on several individual differences in Study 2, such as political orientation and social media engagement (Shen et al., 2019), which can affect information processing.”
Fake news, disinformation and misinformation in social media: a review (2023, unknown): “…studies , which investigate the effect of signals (e.g., sources) to detect and recognize fake news (Vraga and Bode 2017; Jakesch et al. 2019; Shen et al. 2019; Avram et al. 2020; Hameleers et al. 2020; Dias et al. 2020; Nyhan et al. 2020; Bode and Vraga 2015; Tsang 2020; Vishwakarma et al.…”