Fake reviews can mislead consumers into making wrong or suboptimal decisions. Despite the tremendous effort in advancing fake review detection models, existing work has focused on developing complex models to improve the detection performance (e.g., accuracy); however, there has been little research on discovering knowledge about fake or authentic reviews that can guide the detection efforts from those complex models. In addition, previous detection models treat online reviews as the unit of analysis while ignoring the semantic coherence of different sentences within the same review. Using a combination of deep learning with transformers and unsupervised learning, we propose a model-agnostic and data-independent knowledge discovery method for fake review detection that consists of two components: Domain Adaptation Transfer Learning (DATL) and Fine-Grained Knowledge Discovery (FGKD). DATL encodes review texts by adapting a transformer-based model to the context of online reviews. Drawing on the coherence principle from a deception theory, FGKD first identifies review sentences containing tacit knowledge and then uses them to discover knowledge in support of fake review detection. We further develop novel evaluation metrics beyond accuracy for the generated knowledge by extending those from interpretable machine learning. The empirical evaluation results demonstrate consistently positive effects of our knowledge discovery method on the efficiency of fake review detection without hurting model effectiveness. This study not only contributes to interpretable machine learning but also lays the foundation for AI model design to augment humans’ ability in fake review detection.
Tao et al. (Mon,) studied this question.