基于异常评分偏差的垃圾意见检测
Detection of opinion spam based on anomalous rating deviation
摘要：
既得利益者发布虚假评论已经成为消费者在决策中使用在线产品评论的严重问题。为了解决这个问题，已经提出了许多方法来检测这些虚假评论，称为意见垃圾。然而，到目前为止，这些方法中的许多都集中在对综述文本的分析上，使得它们不适合于许多综述系统，其中，伴随文本是可选的，或者是不可能的。此外，这些方法通常在计算上是昂贵的，需要大量的资源来处理通常涉及的数据规模上的文本分析。在本文中，我们认为意见垃圾邮件制造者操纵的平均收视率的产品，专注于垃圾邮件评级和诚实的评论家的大多数意见之间的差异。我们提出了一个轻量级的，有效的方法来检测意见垃圾邮件发送者的基础上，这些差异。该方法使用二项回归来识别具有偏离大多数意见的异常比例的评级的评审员。在真实数据和合成数据上的实验表明，该方法能够成功地识别意见垃圾邮件发送者。与目前的最先进的方法，也只基于评级的比较表明，我们的方法是能够实现类似的检测精度，同时消除了需要对垃圾邮件和非垃圾邮件的评论的概率，并减少了学习所需的繁重的计算。
英文摘要：
The publication of fake reviews by parties with vested interests has become a severe problem for consumers who use online product reviews in their decision making. To counter this problem a number of methods for detecting these fake reviews, termed opinion spam, have been proposed. However, to date, many of these methods focus on analysis of review text, making them unsuitable for many review systems where accom-panying text is optional, or not possible. Moreover, these approaches are often computationally expensive, requiring extensive resources to handle text analysis over the scale of data typically involved. In this paper, we consider opinion spammers manipulation of average ratings for products, focusing on dif-ferences between spammer ratings and the majority opinion of honest reviewers. We propose a lightweight, effective method for detecting opinion spammers based on these differences. This method uses binomial regression to identify reviewers having an anomalous proportion of ratings that deviate from the majority opinion. Experiments on real-world and synthetic data show that our approach is able to successfully iden-tify opinion spammers. Comparison with the current state-of-the-art approach, also based only on ratings, shows that our method is able to achieve similar detection accuracy while removing the need for assump-tions regarding probabilities of spam and non-spam reviews and reducing the heavy computation required for learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
