Distinguishing Human- and AI-Generated Reviews: The Effects of Textual Valence and Reviewer Image Evaluation on Perceived Authenticity
Distinguishing Human- and AI-Generated Reviews: The Effects of Textual Valence and Reviewer Image Evaluation on Perceived Authenticity
Sohyun Park (PhD Student), Bartosz Wojdynski, Jiwon Kim (PhD Student), and Moses U. Okocha (PhD Student), “Distinguishing Human- and AI-Generated Reviews: The Effects of Textual Valence and Reviewer Image Evaluation on Perceived Authenticity. Paper presented at the 2025 AEJMC Midwinter conference, March 7-8, 2025, Norman, OK.
Abstract: Witnessing increasingly prevalent artificial intelligence (AI)-powered fake reviews, this study examines how message factors influence user judgment in assessing the perceived authenticity of review and reviewer credibility in the context of online consumer reviews. The experiment employs a 2 (review text provenance: human-generated vs. AI-generated) × 2 (review valence: positive vs. negative) × 2 (image provenance: human-generated vs. AI-generated) × 2 (image presence: profile only vs. profile and product images) mixed factorial design, integrating eye-tracking technology to examine how users allocate attention when distinguishing between human- and AI-generated reviews.
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