Al-Adhaileh Mosleh Hmoud, Aldhyani Theyazn H H, Alghamdi Ans D
E-Learning and Distance Education, King Faisal University, Saudi Arabia, P.O. Box 4000 Al-Ahsa, Saudi Arabia.
Applied College in Abqaiq, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia.
Appl Bionics Biomech. 2022 Jun 8;2022:4637594. doi: 10.1155/2022/4637594. eCollection 2022.
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.
本文的重点是在在线讨论以及诸如Reddit等社交新闻聚合器上的链接分发中，检测评论者和用户中的恶意捣乱者。恶意捣乱者作为可疑评论者的一个子集，一直是我们关注的焦点。通过使用情感分析和深度学习技术来识别其恶意捣乱帖子的情感，将恶意捣乱评论者与普通评论者区分开来。机器学习和基于词典的方法也可用于情感分析。所提出系统的新颖之处在于，它应用了一种集成双向长短期记忆的卷积神经网络（CNN-BiLSTM）模型，使用从Reddit社交媒体平台收集的标准在线恶意捣乱评论者数据集，来检测在线讨论中的恶意捣乱评论者。我们的工作进行了两项实验：第一项基于文本数据（情感分析），第二项基于从数据集中提取的数值数据（10个属性）。CNN-BiLSTM模型使用文本数据时准确率达到97%，使用数值数据时准确率达到100%。在分析我们模型的结果时，我们观察到它比所比较的方法提供了更好的结果。
