  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.
