Detecting deceptive review spam via attention-based neural networks

Abstract

In recent years, the influence of deceptive review spam has further strengthened in purchasing decisions, election choices and product design. Detecting deceptive review spam has attracted more and more researchers. Existing work makes utmost efforts to explore effective linguistic and behavioral features, and utilizes the off-the-shelf classification algorithms to detect spam. But the models are usually compromised training results on the whole datasets. They failed to distinguish whether a review is linguistically suspicious or behaviorally suspicious or both. In this paper, we propose an attention-based neural networks to detect deceptive review spam by distinguishingly using linguistic and behavioral features. Experimental results on real commercial public datasets show the effectiveness of our model over the state-of-the-art methods.