A Hybridized Approach for Enhanced Fake Review Detection

IF 4.5 2区 计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS IEEE Transactions on Computational Social Systems Pub Date : 2024-06-26 DOI:10.1109/TCSS.2024.3411635
Shu Xu;Haoqi Cuan;Zhichao Yin;Chunyong Yin
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Abstract

User reviews on online consumption platforms are crucial for both consumers and merchants, serving as a reference for purchase decisions and product improvement. However, fake reviews can mislead consumers and harm merchant profits and reputation. Developing effective methods for detecting deceptive reviews is crucial to protecting the interests of both parties. In recent years, research on fake review detection has focused on improving machine learning and neural network methods to enhance the accuracy of fake review detection, neglecting the fundamental and necessary work of text feature representation for reviews. High-quality review text feature representation affects or even determines the quality and performance of fake review detection methods. The increasing prevalence of fake reviews results in a more complex distribution within the feature space of review texts, thus necessitating review embedding methods that exhibit comprehensive semantic comprehension and contextual awareness of review texts. To improve the quality of textual feature representation, we propose a review-embedding attention-based long short-term memory (A-LSTM) method that can encode the global semantics of reviews and detect the deception of the review content. A-LSTM uses attention gates to discover the importance of words, and by analyzing the importance of words, it can help distinguish the characteristics of real and fake reviews, and we propose an attention loss function to solve the problem of class imbalance. On the Yelp dataset, the accuracy of deceptive review detection has increased to 90.9%.
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一种增强虚假评论检测的杂交方法
在线消费平台上的用户评论对消费者和商家都至关重要,可以作为购买决策和产品改进的参考。然而,虚假评论会误导消费者,损害商家的利润和声誉。开发有效的检测欺骗性评论的方法对于保护双方的利益至关重要。近年来,关于虚假评论检测的研究主要集中在改进机器学习和神经网络方法来提高虚假评论检测的准确性,而忽略了评论文本特征表示的基础和必要工作。高质量的评论文本特征表示影响甚至决定了虚假评论检测方法的质量和性能。虚假评论的日益流行导致评论文本特征空间内的分布更加复杂,因此需要对评论文本具有全面语义理解和上下文感知的评论嵌入方法。为了提高文本特征表示的质量,我们提出了一种基于评论嵌入注意力的长短期记忆(a - lstm)方法,该方法可以对评论的全局语义进行编码,并检测评论内容的欺骗。A-LSTM使用注意门来发现单词的重要性,通过分析单词的重要性,可以帮助区分真假评论的特征,我们提出了一个注意损失函数来解决类不平衡的问题。在Yelp数据集上,欺骗性评论检测的准确率提高到了90.9%。
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来源期刊
IEEE Transactions on Computational Social Systems
IEEE Transactions on Computational Social Systems Social Sciences-Social Sciences (miscellaneous)
CiteScore
10.00
自引率
20.00%
发文量
316
期刊介绍: IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.
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