The Affective Iceberg Model for Review Manipulation Detection: A GPT-Based Design Artifact

Online review manipulation poses significant threat to the digital trust in tourism industry. Inspired by iceberg metaphor, this study conceptualizes emotional clues of deception into obvious, surface-level emotional signals and subtle, deeply embedded affective information. To capture the full affective architecture, we propose an Affective Iceberg Model (AIM) for review manipulation detection (AIM-RMD) incorporating GPT-based Multidimensional Affective Extraction (MAE) pipelines. Evaluated on Chinese hotel reviews, AIM-RMD achieved an accuracy of 0.8875 and significantly outperformed existing benchmarks. Detailed module-level analysis revealed that emotional diversity and specific fine-grained emotions serve as critical differentiators between genuine and fake reviews. Additionally, a second experiment demonstrated the model’s strong adaptability through GPT prompt engineering across languages and tourism sub-sectors. Our work provides novel insights into how manipulators’ emotional expressions differ from those of real users, and offers a scalable, explainable, and cross-culturally applicable solution for identifying fake reviews on hotel platforms and broader tourism entities.