Decision Support Systems ( IF 7.5 ) Pub Date : 2022-11-21 , DOI: 10.1016/j.dss.2022.113911 Dong Zhang, Wenwen Li, Baozhuang Niu, Chong Wu
Ensuring the credibility of online consumer reviews (OCRs) is a growing societal concern. However, the problem of fake reviewers on online platforms significantly influences e-commerce authenticity and consumer trust. Existing studies for fake reviewer detection mainly focus on deriving novel behavioral and linguistic features. These features require extensive human labor and expertise, placing a heavy burden on platforms. Therefore, we propose a novel end-to-end framework to detect fake reviewers based on behavior and textual information. It has two key components: (1) a behavior-sensitive feature extractor that learns the underlying patterns of reviewing behavior; (2) a context-aware attention mechanism that extracts valuable features from online reviews. We rigorously evaluate each proposed module and the entire framework against state-of-the-art benchmarks on two real-world datasets from http://Yelp.com. Experimental results demonstrate that our method achieves state-of-the-art results on fake reviewer detection. Our method can be considered a tentative step toward lowering human labor costs in realizing automated fake reviewer detection on e-commerce platforms.
中文翻译:
一种检测虚假评论者的深度学习方法:利用评论行为和文本信息
确保在线消费者评论 (OCR) 的可信度是一个日益增长的社会问题。然而,在线平台上的虚假评论者问题严重影响电子商务的真实性和消费者信任。现有的虚假评论者检测研究主要集中在推导新的行为和语言特征上。这些功能需要大量的人力和专业知识,给平台带来了沉重的负担。因此,我们提出了一种新颖的端到端框架来检测基于行为和文本信息的虚假评论者。它有两个关键组件:(1) 一个行为敏感的特征提取器,可以学习审查行为的潜在模式;(2) 上下文感知注意机制,从在线评论中提取有价值的特征。我们根据来自 http://Yelp.com 的两个真实世界数据集的最先进基准严格评估每个提议的模块和整个框架。实验结果表明,我们的方法在虚假评论者检测方面取得了最先进的结果。我们的方法可以被认为是在电子商务平台上实现自动虚假评论者检测以降低人力成本的尝试性步骤。