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.

翻译：欺骗性评论日益普遍，尤其是在大语言模型性能提升及广泛应用后。现有研究主要关注区分真实与欺骗性人类评论的模型开发，但对真实评论与AI生成虚假评论之间的差异知之甚少。此外，目前大部分研究集中于英语领域，对其他语言的研究极为有限。本文构建并向公众发布MAiDE-up数据集，包含10,000条真实酒店评论与10,000条AI生成的虚假酒店评论，在十种语言中保持平衡。利用该数据集，我们进行广泛的语言学分析，旨在：(1) 比较AI虚假酒店评论与真实酒店评论的差异；(2) 识别影响欺骗检测模型性能的因素。我们从情感、地点和语言三个主要维度探索多种模型在酒店评论欺骗检测中的有效性，发现这些维度显著影响AI生成虚假评论的检测能力。