mlsanityData to 14 Oct 2026

MAiDE-up: Multilingual Deception Detection of GPT-generated Hotel Reviews

arXiv 2404.12938 ↗First posted 19 Apr 2024cs.CL cs.AIcited by 5

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 25 of its 60 references that are in the map. First posted on arXiv 19 Apr 2024.

Builds on

25 of 60 references.

References · 25

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  4. 2023DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvaturecited by 726
  5. 2019CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Datacited by 548
  6. 2019Release Strategies and the Social Impacts of Language Modelscited by 513
  7. 2023How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detectioncited by 481
  8. 2023Can AI-Generated Text be Reliably Detected?cited by 386
  9. 2019Automatic Detection of Generated Text is Easiest when Humans are Fooledcited by 261
  10. 2023RADAR: Robust AI-Text Detection via Adversarial Learningcited by 190
  11. 2020Automatic Detection of Machine Generated Text: A Critical Surveycited by 166
  12. 2023M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detectioncited by 121
13 more
  1. 2020TweepFake: about Detecting Deepfake Tweetscited by 117
  2. 2023Intrinsic Dimension Estimation for Robust Detection of AI-Generated Textscited by 112
  3. 2022Human heuristics for AI-generated language are flawedcited by 105
  4. 2019Real or Fake? Learning to Discriminate Machine from Human Generated Textcited by 99
  5. 2022Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generationcited by 99
  6. 2021Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Textcited by 89
  7. 2023OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated Examplescited by 80
  8. 2023Fighting Fire with Fire: Can ChatGPT Detect AI-generated Text?cited by 39
  9. 2021The Threat of Offensive AI to Organizationscited by 38
  10. 2023Towards a Robust Detection of Language Model Generated Text: Is ChatGPT that Easy to Detect?cited by 23
  11. 2017Scattertext: a Browser-Based Tool for Visualizing how Corpora Differcited by 22
  12. 2023TextDescriptives: A Python package for calculating a large variety of metrics from textcited by 13
  13. 2023Evaluating ChatGPT as a Question Answering System: A Comprehensive Analysis and Comparison with Existing Modelscited by 11

Built on by

Cited by year

Citing papers per year: 2024: 3; 2025: 2.

5 papers.