Abstract
Recent advances in generative models such as GPT may be used to fabricate indistinguishable fake customer reviews at a much lower cost, posing challenges for social media platforms to detect this kind of content. This study addresses two research questions: (1) the effective detection of AI-generated restaurant reviews generated from high-quality elite authentic reviews, and (2) the comparison of out-of-sample predicted AI-generated reviews and authentic reviews across multiple dimensions of review, user, restaurant, and content characteristics. We fine-tuned a GPT text detector to predict fake reviews, significantly outperforming existing solutions. We applied the model to predict non-elite reviews that already passed the Yelp filtering system, revealing that AI-generated reviews typically score higher ratings, users posting such content have less established Yelp reputations and AI-generated reviews are more comprehensible and less linguistically complex than human-generated reviews. Notably, machine-generated reviews are more prevalent in low-traffic restaurants in terms of customer visits.
| Original language | English |
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| Title of host publication | International Conference on Information Systems (ICIS) 2023 |
| Number of pages | 17 |
| Publication status | Published - 11 Dec 2023 |
| Event | International Conference on Information Systems - Hyderabad, India Duration: 10 Dec 2023 → 13 Dec 2023 Conference number: 43 https://icis2023.aisconferences.org/ |
Conference
| Conference | International Conference on Information Systems |
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| Abbreviated title | ICIS |
| Country/Territory | India |
| City | Hyderabad |
| Period | 10/12/23 → 13/12/23 |
| Internet address |