Original Study

Shen, C., Kasra, M., Pan, W., Bassett, G. A., Malloch, Y., & O’Brien, J. F. (2019). Fake images: The effects of source, intermediary, and digital media literacy on contextual assessment of image credibility online. New Media & Society, 21(2), 438–463. https://doi.org/10.1177/1461444818799526

Abstract. 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.

Open original study

Replication

Class activity, Mario Haim, 2025

Replicators: Haim, Mario; Knöpfle, Philipp; Breuer, Johannes

Abstract. Alarmingly low replication rates have been highlighted across disciplines. In communication science, rates are unknown but replications have some tradition. However, due to the field's need to adapt to rapidly changing research contexts, replications have largely been conceptual rather than direct. We conduct two in-depth replications to systematically evaluate both forms. For that, we consider online information credibility as rapidly changing research context, serving as signpost for people to regulate cognitive capacities and navigate available information. In one study, Shen and colleagues (2019) examined US participants' credibility of manually fabricated news images. We assess the robustness and generalizability of their findings through a direct (identical stimuli, US sample) and a conceptual (AI-generated fake images, German sample) replication, successfully replicating 71 percent of the original study's empirical claims through our direct and 86 percent through our conceptual replication. Our reflection echoes recent calls for more open science and dedicated data editors.