针对在线反意见垃圾:从评论序列中发现虚假评论

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI:10.1109/ASONAM.2014.6921594

Yuming Lin, Tao Zhu, Hao Wu, Jingwei Zhang, Xiaoling Wang, Aoying Zhou

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引用次数: 73

摘要

检测评论垃圾邮件对于当前的电子商务应用程序非常重要。然而，以往的工作忽略了发布的审查顺序。在本文中，我们探讨了评论序列中的虚假评论检测问题，这是实施在线反意见垃圾邮件的关键。我们首先分析虚假评论的特点。基于评论内容和评论者行为，提出了六个时间敏感特征来突出虚假评论。然后，我们设计监督解决方案和基于阈值的解决方案，以便尽早发现虚假评论。实验结果表明，该方法能够有序地识别虚假评论，具有较高的准确率和召回率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Towards online anti-opinion spam: Spotting fake reviews from the review sequence

Detecting review spam is important for current e-commerce applications. However, the posted order of review has been neglected by the former work. In this paper, we explore the issue on fake review detection in review sequence, which is crucial for implementing online anti-opinion spam. We analyze the characteristics of fake reviews firstly. Based on review contents and reviewer behaviors, six time sensitive features are proposed to highlight the fake reviews. And then, we devise supervised solutions and a threshold-based solution to spot the fake reviews as early as possible. The experimental results show that our methods can identify the fake reviews orderly with high precision and recall.

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来源期刊

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)

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