To address the first challenge, the feature learning of behavioral features can be leveraged to improve detection performance. Feature learning is characterized by learning representations for specific tasks from raw data [16]. Compared with deriving novel behavioral features, feature learning requires less human labor, expertise, and can learn the underlying patterns of raw behavioral data. To address the second issue, we leverage the most advanced pre-trained language model, Longformer [17], to generate contextualized text representations from online reviews. Compared with traditional linguistic features, contextualized text representation can capture more semantic information from text inputs [18]. We then can utilize deep learning models to extract valuable features from the contextualized text representations and perform corresponding classification tasks. Therefore, we propose a novel deep learning-based framework for fake reviewer detection. The framework has two key novelties:
The authors are grateful to the editor and reviewers for their helpful comments. This work was supported by the National Natural Science Foundation of China (72201105, 72125006).
Dong Zhang is currently working as a postdoc in the School of Business Administration, South China University of Technology, Guangzhou, China. His papers have been published at journals such as Journal of the Operational Research Society and Kybernetes. His research interests include data mining methods and online reviews.
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