Reliable fake review detection via modeling temporal and behavioral patterns
Reliable fake review detection via modeling temporal and behavioral patterns
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DOI：
10.1109/bigdata.2017.8257963
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发表时间：
2017-12
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通讯作者：
X. Wu;Yuxiao Dong;Jun Tao;Chao Huang;N. Chawla
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文献类型：
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作者：
X. Wu;Yuxiao Dong;Jun Tao;Chao Huang;N. Chawla
虚假评论已经成为在线评论系统中普遍存在的问题，其中欺诈性用户操纵对象的感知（例如，一家餐馆）编造虚假评论。大量的工作致力于通过分别对不同因素（例如用户特征、对象特征和用户-对象二分关系）进行建模来识别虚假评论。然而，这个问题仍然具有挑战性，因为事实上，更先进的伪装策略被恶意用户利用。在现实世界中，垃圾邮件发送者可能会假装是正常用户，给出与正常用户相似的分数分布的虚假评论。为了解决这些问题，我们建议探索用户的评论行为的时间模式，因为垃圾邮件发送者更喜欢在短时间内提升或降级的目标企业。在这项工作中，我们提出了一个统一的框架可靠的虚假审查检测（RFRD），明确建模的时间模式的用户的审查行为到一个概率生成模型。此外，RFRD框架模型用户的基本审查的可信度和对象的高度倾斜的审查分布。我们在两个Yelp数据集上进行了实验，证明了所提出的RFRD框架的有效性。
Fake reviews have become a pervasive problem in online review systems, wherein fraudulent users manipulate the perception of an object (e.g., a restaurant) by fabricating fake reviews. Extensive work has been devoted to identifying fake reviews via modeling different factors separately, such as user features, object characteristics, and user-object bipartite relations. However, this problem remains challenging due to the fact that more advanced camouflage strategies are utilized by malicious users. In real-world scenarios, spammers may pretend to be normal users by giving fake reviews with the similar score distribution as normal users. To address these issues, we propose to explore the temporal patterns of users' review behavior, because spammers prefer to promote or demote the target businesses in a short period of time. In this work, we present a unified framework Reliable Fake Review Detection (RFRD) that explicitly models temporal patterns of users' review behavior into a probabilistic generative model. Moreover, the RFRD framework models users' underlying review credibility and objects' highly-skewed review distributions. We conduct experiments on two Yelp datasets, demonstrating the effectiveness of the proposed RFRD framework.