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Creating and detecting fake reviews of online products
Journal of Retailing and Consumer Services ( IF 10.4 ) Pub Date : 2021-09-20 , DOI: 10.1016/j.jretconser.2021.102771
Joni Salminen 1, 2 , Chandrashekhar Kandpal 3 , Ahmed Mohamed Kamel 4 , Soon-gyo Jung 1 , Bernard J. Jansen 1
Affiliation  

Customers increasingly rely on reviews for product information. However, the usefulness of online reviews is impeded by fake reviews that give an untruthful picture of product quality. Therefore, detection of fake reviews is needed. Unfortunately, so far, automatic detection has only had partial success in this challenging task. In this research, we address the creation and detection of fake reviews. First, we experiment with two language models, ULMFiT and GPT-2, to generate fake product reviews based on an Amazon e-commerce dataset. Using the better model, GPT-2, we create a dataset for a classification task of fake review detection. We show that a machine classifier can accomplish this goal near-perfectly, whereas human raters exhibit significantly lower accuracy and agreement than the tested algorithms. The model was also effective on detected human generated fake reviews. The results imply that, while fake review detection is challenging for humans, “machines can fight machines” in the task of detecting fake reviews. Our findings have implications for consumer protection, defense of firms from unfair competition, and responsibility of review platforms.



中文翻译:

创建和检测在线产品的虚假评论

客户越来越依赖评论来获取产品信息。然而,在线评论的有用性受到虚假评论的阻碍,这些虚假评论提供了对产品质量的不真实描述。因此,需要检测虚假评论。不幸的是,到目前为止,自动检测在这项具有挑战性的任务中只取得了部分成功。在这项研究中,我们解决了虚假评论的创建和检测问题。首先,我们使用两种语言模型 ULMFiT 和 GPT-2 进行实验,以基于亚马逊电子商务数据集生成虚假产品评论。使用更好的模型 GPT-2,我们为虚假评论检测的分类任务创建了一个数据集。我们表明,机器分类器可以近乎完美地实现这一目标,而人类评估者的准确性和一致性明显低于测试算法。该模型对检测到的人工生成的虚假评论也很有效。结果表明,虽然虚假评论检测对人类具有挑战性,但在检测虚假评论的任务中,“机器可以对抗机器”。我们的研究结果对消费者保护、保护公司免受不公平竞争以及审查平台的责任有影响。

更新日期:2021-09-20
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