Detecting Anomalous Online Reviewers: An Unsupervised Approach Using Mixture Models

• 成果类型: Article
• 署名作者: Kumar, Naveen; Venugopal, Deepak; Qiu, Liangfei; Kumar, Subodha
• 署名单位: University of Washington; University of Washington Bothell; University of Memphis; State University System of Florida; University of Florida; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2019.1661089
• 发表日期: 2019
• 页码: 1313-1346
• 关键词: word-of-mouth; deception; manipulation; platforms; fake; strategies; support; IMPACT; sales; Chat

摘要

Online reviews play a significant role in influencing decisions made by users in day-to-day life. The presence of reviewers who deliberately post fake reviews for financial or other gains, however, negatively impacts both users and businesses. Unfortunately, automatically detecting such reviewers is a challenging problem since fake reviews do not seem out-of-place next to genuine reviews. In this paper, we present a fully unsupervised approach to detect anomalous behavior in online reviewers. We propose a novel hierarchical approach for this task in which we (1) derive distributions for key features that define reviewer behavior, and (2) combine these distributions into a finite mixture model. Our approach is highly generalizable and it allows us to seamlessly combine both univariate and multivariate distributions into a unified anomaly detection system. Most importantly, it requires no explicit labeling (spam/not spam) of the data. Our newly developed approach outperforms prior state-of-the-art unsupervised anomaly detection approaches.