A Multifaceted Novel Approach to Identify Deceptive Reviews Based on Psychology Theories: A New Dataset - Asiri, Abeer Hassan; Alotaibi, Fahad Mazaed (Communications in Mathematics and Applications, 2025)
As deception negatively impacts various areas, deception detection is an important field of study. This paper introduces a framework to detect online review deception. It studied aspects of deceptive reviews, considering the complex nature of deception in textual data and the low chance of direct detection. Furthermore, the paper presents a new corpus for deceptive reviews labeled using deception hints. This dataset was compiled in English and extracted from Google Maps reviews. We focused on the reviews of “restaurants” in New York. The novelty of this dataset is that the truthful and deceptive reviews were not deliberately collected; that is, participants were not requested to write lies, but the texts were collected after the individuals had written them. We used predefined criteria using deception indicators to differentiate deceptive and truthful reviews for this dataset. Each suggested indicator is not a definitive indicator on its own, but we assessed review authenticity using a set of indicators together. This paper aims to discuss lie detection strategies and theories, design a theory-based framework to detect deception in online reviews and implement the framework on a real-world dataset to provide a foundation for future empirical research and practical applications. The experimental results obtained from our labeled benchmark dataset showcase the effectiveness of this approach.