Fake review and reviewer detection through behavioral graph partitioning integrating deep neural network

With a profound effect of online reviews on customers’ decisions about purchasing products or services, untruthful (fake) reviews written to deceive product quality and receive unfair commercial benefits have become a crucial problem. In this work, we propose a graph partitioning approach (BeGP) and its extension (BeGPX) to distinguish fake reviewers from benign ones. The main idea of BeGP is to first construct a behavioral graph in which reviewers are connected if they share common characteristic features that capture their similar behavior. Then, the algorithm starts with a small subgraph of known fake reviewers and afterwards repeatedly expands the subgraph by inducing other connected suspicious reviewers. Subsequently, all reviews of those suspects are hypothesized to be untruthful. Moreover, to enhance the performance of fake review(er) detection, BeGPX employs additional analysis of semantic content and emotions expressed in reviews. In particular, we use the deep neural network to learn word embeddings representation and lexicon-based emotion indicators in order to integrate into the graph construction process. We demonstrate the effectiveness of BeGP and BeGPX on two real-world review datasets from Yelp.com. The results show that both approaches outperform state-of-the-art methods with accurately identifying fake review(er)s within the k -first order of rankings. In addition, BeGPX shows significant enhancement although being provided with only a few amount of learning labeled data.