Multi-task ensemble learning for fake reviews detection and helpfulness prediction: a novel approach

Research on fake reviews detection and review helpfulness prediction is prevalent, yet most studies tend to focus solely on either fake reviews detection or review helpfulness prediction, considering them separate research tasks. In contrast to this prevailing pattern, we address both challenges concurrently by employing a multi-task learning approach. We posit that undertaking these tasks simultaneously can enhance the performance of each task through shared information among features. We utilize pre-trained RoBERTa embeddings with a document-level data representation. This is coupled with an array of deep learning and neural network models, including Bi-LSTM, LSTM, GRU, and CNN. Additionally, we employ ensemble learning techniques to integrate these models, with the objective of enhancing overall prediction accuracy and mitigating the risk of overfitting. The findings of this study offer valuable insights to the fields of NLP and machine learning and present a novel perspective on leveraging multi-task learning for the twin challenges of fake reviews detection and review helpfulness prediction.