Online Troll Reviewer Detection Using Deep Learning Techniques. (8th June 2022)

• Record Type: Journal Article
• Authors: Al-Adhaileh, Mosleh Hmoud; Aldhyani, Theyazn H. H.; Alghamdi, Ans D.
• Other Names: Algalil Fahd Abd Academic Editor.
• Is Part Of: Applied bionics and biomechanics. Volume 2022(2022)
• Publication Date: 2022-06-08
• Subjects: Bionics -- Periodicals; Biomechanics -- Periodicals; Biomedical engineering -- Periodicals; 003.505
• DOI: 10.1155/2022/4637594
• Languages: English
• ISSNs: 1176-2322

Abstract

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.