DeepTrust: An Automatic Framework to Detect Trustworthy Users in Opinion-based Systems
DeepTrust: An Automatic Framework to Detect Trustworthy Users in Opinion-based Systems
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DeepTrust：在基于意见的系统中检测可信用户的自动框架
DOI：
10.1145/3374664.3375744
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Squicciarini, Anna
中科院分区：
文献类型：
--
作者：
Serra, Edoardo;Shrestha, Anu;Spezzano, Francesca;Squicciarini, Anna
随着越来越多的在线平台依靠用户的意见来帮助潜在客户对产品和服务做出明智的决定，观点垃圾邮件最近得到了关注。然而，尽管垃圾评论的工作比比皆是，但大多数努力都集中在发现个别评论者是垃圾邮件发送者或欺诈者。我们认为，这已经不够了，因为评论员可能以各种方式为基于意见的系统做出贡献，他们的输入可能从高度信息量到嘈杂甚至恶意。为了改进基于意见的系统中可信个体的检测，在本文中，我们开发了一种有监督的方法来区分不同类型的评论者。具体地说，我们将检测值得信赖的评论者的问题建模为一个多类分类问题，其中用户可能是欺诈性的、不可靠的、不提供信息的或值得信赖的。我们注意到，从经典的值得信赖/不值得信赖(或恶意)评审者的二进制分类扩展是一个有趣且具有挑战性的问题。一些不值得信赖的评论者的行为可能与可靠的评论者相似，但却植根于黑暗的动机。相反，其他不值得信任的评论者可能不是恶意的，而是相当懒惰或无法为评论项目的共同知识做出贡献。我们提出的方法DeepTrust依赖于一个深度递归神经网络，该网络提供聚合时间信息的嵌入：我们考虑用户在一段时间内的行为，因为他们评论多个产品。我们使用时间二分图对评论者和他们评论的产品之间的交互进行建模，并通过包括其他评论者对相同项目的评分来考虑每个评级的上下文。我们在亚马逊评论者的真实数据集上进行了广泛的实验，使用有关垃圾邮件发送者和欺诈性评论的已知基本事实。我们的结果表明，DeepTrust可以检测到值得信赖的、不提供信息的和欺诈性的用户，F1度量为0.93。此外，与Rev2最先进的方法(AUROC为0.79，平均精度为0.48)相比，我们在检测欺诈性评论者方面有了显著的改进(AUROC为0.97，当结合DeepTrust和F&G算法时，平均精度为0.99)。此外，DeepTrust对冷启动用户是稳健的，表现优于所有现有的基准。
Opinion spamming has recently gained attention as more and more online platforms rely on users' opinions to help potential customers make informed decisions on products and services. Yet, while work on opinion spamming abounds, most efforts have focused on detecting an individual reviewer as spammer or fraudulent. We argue that this is no longer sufficient, as reviewers may contribute to an opinion-based system in various ways, and their input could range from highly informative to noisy or even malicious. In an effort to improve the detection of trustworthy individuals within opinion-based systems, in this paper, we develop a supervised approach to differentiate among different types of reviewers. Particularly, we model the problem of detecting trustworthy reviewers as a multi-class classification problem, wherein users may be fraudulent, unreliable or uninformative, or trustworthy. We note that expanding from the classic binary classification of trustworthy/untrustworthy (or malicious) reviewers is an interesting and challenging problem. Some untrustworthy reviewers may behave similarly to reliable reviewers, and yet be rooted by dark motives. On the contrary, other untrustworthy reviewers may not be malicious but rather lazy or unable to contribute to the common knowledge of the reviewed item. Our proposed method, DeepTrust, relies on a deep recurrent neural network that provides embeddings aggregating temporal information: we consider users' behavior over time, as they review multiple products. We model the interactions of reviewers and the products they review using a temporal bipartite graph and consider the context of each rating by including other reviewers' ratings of the same items. We carry out extensive experiments on a real-world dataset of Amazon reviewers, with known ground truth about spammers and fraudulent reviews. Our results show that DeepTrust can detect trustworthy, uninformative, and fraudulent users with an F1-measure of 0.93. Also, we drastically improve on detecting fraudulent reviewers (AUROC of 0.97 and average precision of 0.99 when combining DeepTrust with the F&G algorithm) as compared to REV2 state-of-the-art methods (AUROC of 0.79 and average precision of 0.48). Further, DeepTrust is robust to cold start users and overperforms all existing baselines.
根据信任评分查找网络中节点的偏见和声望
影响因子：
2.5
作者：
Abhinav Mishra;Arnab Bhattacharya
通讯作者：
Arnab Bhattacharya