Fake reviews classification using deep learning ensemble of shallow convolutions

Online reviews have a decisive impact on consumers' purchasing decisions. This opens the doors for spammers and scammers to post fake reviews for promoting non-existent products or undermine competitor products to affect social behavior. Thus, the identification of reviews as fake and real has become ever more important. Traditional approaches for text classification use a bag-of-words model to represent text which causes sparsity and word representations learnt from neural networks with limited ability to handle unknown words. In this paper, we propose a technique based on three different models trained on the idea of a multi-view learning technique and create an ensemble of all models by employing an aggregation technique for generating final predictions. The core idea of our methodology is to extract rich information from the text of reviews by combining bag-of-n-grams and parallel convolution neural networks(CNNs). By using an n-gram embedding layer with small kernel sizes we can use local context with the same computation power as required to train deep and complex CNNs. Our CNN-based architecture consumes n-gram embeddings as input and uses the parallel convolutional blocks to extract richer feature representations from text. Our approach for the detection of fake reviews also combines textual linguistic features and non-textual features related to reviewer behavior. We evaluate our approach on publically available Yelp Filtered Dataset and achieve F1 scores of up to 92% for classifying fake reviews.