题　目：What Online Reviewer Behaviors Really Matter? A Study of Effects of Verbal and Nonverbal Behaviors on Online Fake Review Detection

主讲人：张东松教授（Department of Information Systems, University of Maryland, Baltimore County, USA）

时　间：2016年8月3日（周三）下午3:30

地　点：主楼216

主讲人简介：

    Dr. Dongsong Zhang is a tenured Professor in the Department of Information Systems at the University of Maryland, Baltimore County, USA. He received his Ph.D. in Management Information Systems from the Eller School of Management at the University of Arizona, one of the top five MIS programs in United States. His current research interests include context-aware mobile computing, computer-mediated collaboration and communication, knowledge management, and e-Business. He has published about 100 papers in academic journals, conference proceedings, and book chapters, including premium journals such as as MIS Quarterly, Journal of Management Information Systems (JMIS), Communications of the ACM (CACM), IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Software Engineering, IEEE Transactions on Multimedia, IEEE Transactions on Systems, Man, and Cybernetics, IEEE Transactions on Professional Communication, Decision Support Systems, and Information & Management, among others. He has received research grants and awards from National Institution of Health (NIH), Google Inc., and Chinese Academy of Sciences.

内容简介：

　The value and credibility of online consumer reviews are compromised by significantly increasing yet difficult-to-be-identified fake reviews. Extant models for automated online fake review detection rely heavily on verbal behaviors of reviewers while largely ignoring their nonverbal behaviors. This research identifies a variety of nonverbal behavioral features of online reviewers and examines their relative importance for the detection of fake reviews in comparison to that of verbal behavioral features. The results of an empirical evaluation using real-world online reviews reveal that incorporating nonverbal features of reviewers can significantly improve the performance of online fake review detection models. Moreover, compared with verbal features, nonverbal features of reviewers are shown to be more important for fake review detection.  Furthermore, model pruning based on a sensitivity analysis improves the parsimony of the developed fake review detection models without sacrificing its performance.

（承办：管理科学与工程系、科研与学术交流中心）