Spam detection in reviews using lstm-based multi-entity temporal features

Abstract

Current works on spam detection in product reviews tend to ignore the temporal relevance among reviews in the user or product entity, resulting in poor detection performance. To address this issue, the present paper proposes a spam detection method that jointly learns comprehensive temporal features from both behavioral and text features in user and product entities. We first extract the behavioral features of a single review, then employ a convolutional neural network (CNN) to learn the text features of this review. We next combine the behavioral features with the text features of each review and train a Long-Short-Term Memory (LSTM) model to learn the temporal features of every review in the user and product entities. Finally, we train a classifier using all of the learned temporal features in order to predict whether a particular review is spam. Experimental results demonstrate that the proposed method can effectively extract the temporal features from historical activ-ities, and can further jointly analyze the activity trajectories from multiple entities. Thus, the proposed method significantly improves the spam detection accuracy.