Online Troll Reviewer Detection Using Deep Learning Techniques

Abstract

The concentration of this paper is on detecting trolls among reviewers and users in online discussions and link distribution on social news aggregators such as Reddit. Trolls, a subset of suspicious reviewers, have been the focus of our attention. A troll reviewer is distinguished from an ordinary reviewer by the use of sentiment analysis and deep learning techniques to identify the sentiment of their troll posts. Machine learning and lexicon-based approaches can also be used for sentiment analysis. The novelty of the proposed system is that it applies a convolutional neural network integrated with a bidirectional long short-term memory (CNN-BiLSTM) model to detect troll reviewers in online discussions using a standard troll online reviewer dataset collected from the Reddit social media platform. Two experiments were carried out in our work: the first one was based on text data (sentiment analysis), and the second one was based on numerical data (10 attributes) extracted from the dataset. The CNN-BiLSTM model achieved 97% accuracy using text data and 100% accuracy using numerical data. While analyzing the results of our model, we observed that it provided better results than the compared methods.