Spam review detection using self-organizing maps and convolutional neural networks

Online public reviews have significant influenced customers who purchase products or seek services. Fake reviews are posted online to promote or demote targeted products or repu-tation of the organizations and businesses. Spam review detection has been the focus of many researchers in recent years. As the online services have been growing rapidly, the im-portance of the issue is ever increasing and needs to be addressed properly. In this regard, there is a variety of approaches that have been introduced to distinguish truthful reviews from the fake ones. The main features engineered in the past studies typically involve two types of linguistic-based and behavioral-based characteristics of the reviews. Unsupervised, supervised and semi-supervised machine leaming methods have been widely utilized to perform such a classification. This paper introduces a novel approach to detect fake reviews from the genuine ones using linguistic features. Unsupervised learning via self-organizing maps (SOM) in conjunction with a convolutional neural networks (CNN) are employed to perform classification of the reviews. We transform the reviews into images by arranging semantically-similar words around a pixel of the image or equivalently a SOM grid cell. The resulting review images are consequently fed to the CNN for supervised training and then classification. Comprehensive tests on two gold-standard datasets show the effectiveness of the proposed method on single and multi-domain contexts with accuracy of 88% and 87%, respectively. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Online public reviews play a significant role in influencing consumer behavior, with fake reviews being a prevalent issue that researchers have been addressing. Different approaches have been introduced to distinguish between genuine and fake reviews, including linguistic and behavioral-based features, as well as the application of machine learning methods such as unsupervised and supervised learning. The proposed method in this study utilizes linguistic features and employs a combination of self-organizing maps and convolutional neural networks to achieve high accuracy in classifying reviews.