用于检测欺骗性评论的多视图聚类框架

IF 0.9 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yubao Zhang, Haining Wang, A. Stavrou
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引用次数: 0

摘要

在线评论在当今商业生态系统中发挥着关键作用,已经成为消费者意见的主要来源。由于其重要性,专业评论撰写服务被用于付费评论,甚至被利用来进行意见垃圾邮件。发布虚假评论可能会误导客户,给服务供应商带来重大利益或损失,并侵蚀对整个在线购物生态系统的信心。在本文中,我们找出了来自专业评论撰写服务的欺骗性评论。我们这样做,即使评论者利用一些假名身份来避免检测。为了揭示与欺骗性审稿人相关的假名身份,我们利用了多视图聚类方法。这使我们能够描述审稿人的写作风格(欺骗性的与正常的),并根据他们的写作风格对审稿人进行聚类。此外,我们探索了不同的神经网络模型来模拟欺骗性评论的写作风格。我们选择表现最好的神经网络来生成评论的表示。我们在不同的实验场景下使用真实的亚马逊评论数据验证了多视图聚类框架的有效性。我们的结果表明,我们的方法优于以往的研究。我们通过基于公开可用的Amazon数据集的大规模案例研究进一步证明了它的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A multiview clustering framework for detecting deceptive reviews
Online reviews, which play a key role in the ecosystem of nowadays business, have been the primary source of consumer opinions. Due to their importance, professional review writing services are employed for paid reviews and even being exploited to conduct opinion spam. Posting deceptive reviews could mislead customers, yield significant benefits or losses to service vendors, and erode confidence in the entire online purchasing ecosystem. In this paper, we ferret out deceptive reviews originated from professional review writing services. We do so even when reviewers leverage a number of pseudonymous identities to avoid the detection. To unveil the pseudonymous identities associated with deceptive reviewers, we leverage the multiview clustering method. This enables us to characterize the writing style of reviewers (deceptive vs normal) and cluster the reviewers based on their writing style. Furthermore, we explore different neural network models to model the writing style of deceptive reviews. We select the best performing neural network to generate the representation of reviews. We validate the effectiveness of the multiview clustering framework using real-world Amazon review data under different experimental scenarios. Our results show that our approach outperforms previous research. We further demonstrate its superiority through a large-scale case study based on publicly available Amazon datasets.
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来源期刊
Journal of Computer Security
Journal of Computer Security COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
1.70
自引率
0.00%
发文量
35
期刊介绍: The Journal of Computer Security presents research and development results of lasting significance in the theory, design, implementation, analysis, and application of secure computer systems and networks. It will also provide a forum for ideas about the meaning and implications of security and privacy, particularly those with important consequences for the technical community. The Journal provides an opportunity to publish articles of greater depth and length than is possible in the proceedings of various existing conferences, while addressing an audience of researchers in computer security who can be assumed to have a more specialized background than the readership of other archival publications.
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