在线产品评论中极端评论者群体的检测与特征分析
Detecting and Characterizing Extremist Reviewer Groups in Online Product Reviews
摘要：
在线市场经常以评论的形式见证意见垃圾。人们经常被雇佣来针对特定的品牌，通过撰写高度积极或消极的评论来促进或阻碍他们。这通常是在群体中集体完成的。虽然之前的一些研究试图识别和分析这样的意见垃圾群体，但很少有人探索发现那些针对整个品牌而不仅仅是产品的群体。在本文中，我们收集了来自亚马逊产品评论网站的评论，并手动标记了一组923个候选评论者组。使用频繁项集挖掘的品牌相似性，使用户聚集在一起，如果他们有相互审查（产品）的很多品牌提取组。我们假设，评审组的性质取决于特定于（组，品牌）对的8个特征。我们开发了一个基于特征的监督模型来将候选团体分类为极端主义实体。我们运行多个分类器，根据该组用户撰写的评论对该组进行分类，以确定该组是否显示出极端的迹象。基于3层感知器的分类器是最好的分类器。我们进一步详细研究了这些群体的行为，以更好地了解品牌级意见欺诈的动态。这些行为包括评分的一致性、评论情绪、已验证的购买、评论日期和对评论的有用投票。令人惊讶的是，我们观察到有很多经过验证的评论者表现出极端的情绪，经过进一步调查，这些评论者找到了规避现有机制的方法，以防止亚马逊上的非官方激励措施。
英文摘要：
Online marketplaces often witness opinion spam in the form of reviews. People are often hired to target specific brands for promoting or impeding them by writing highly positive or negative reviews. This often is done collectively in groups. Although some previous studies attempted to identify and analyze such opinion spam groups, little has been explored to spot those groups who target a brand as a whole, instead of just products. In this paper, we collected reviews from the Amazon product review site and manually labelled a set of 923 candidate reviewer groups. The groups are extracted using frequent itemset mining over brand similarities such that users are clustered together if they have mutually reviewed (products of) a lot of brands. We hypothesize that the nature of the reviewer groups is dependent on 8 features specific to a (group, brand) pair. We develop a feature-based supervised model to classify candidate groups as extremist entities. We run multiple classifiers for the task of classifying a group based on the reviews written by the users of that group, to determine if the group shows signs of extremity. A 3-layer Perceptron based classifier turns out to be the best classifier. We further study the behaviours of such groups in detail to understand the dynamics of brand-level opinion fraud better. These behaviours include consistency in ratings, review sentiment, verified purchase, review dates and helpful votes received on reviews. Surprisingly, we observe that there are a lot of verified reviewers showing extreme sentiment, which on further investigation leads to ways to circumvent existing mechanisms in place to prevent unofficial incentives on Amazon.
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。
