MAiDE-up: Multilingual Deception Detection of AI-generated Hotel Reviews
Oana Ignat, Xiaomeng Xu, Rada Mihalcea
Abstract
Deceptive reviews are becoming increasingly common, especially given the increase in performance and the prevalence of LLMs. While work to date has addressed the development of models to differentiate between truthful and deceptive human reviews, much less is known about the distinction between real reviews and AI-authored fake reviews. Moreover, most of the research so far has focused primarily on English, with very little work dedicated to other languages. In this paper, we compile and make publicly available the MAiDE-up dataset, consisting of 10,000 real and 10,000 AI-generated fake hotel reviews, balanced across ten languages. Using this dataset, we conduct extensive linguistic analyses to (1) compare the AI fake hotel reviews to real hotel reviews, and (2) identify the factors that influence the deception detection model performance. We explore the effectiveness of several models for deception detection in hotel reviews across three main dimensions: sentiment, location, and language. We find that these dimensions influence how well we can detect AI-generated fake reviews.
BibTeX
@inproceedings{ignat-etal-2025-maide,
title = "{MA}i{DE}-up: Multilingual Deception Detection of {AI}-generated Hotel Reviews",
author = "Ignat, Oana and
Xu, Xiaomeng and
Mihalcea, Rada",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.88/",
pages = "1636--1653",
ISBN = "979-8-89176-195-7"
}