What Online Reviewer Behaviors Really Matter? Effects of Verbal and Nonverbal Behaviors on Detection of Fake Online Reviews

被引：197
作者：
Zhang, Dongsong [1 ,2 ]
Zhou, Lina [2 ]
Kehoe, Juan Luo [2 ]
Kilic, Isil Yakut [2 ]
机构：
[1] Jinan Univ, Int Business Sch, Guangzhou Shi, Guangdong Sheng, Peoples R China
[2] Univ Maryland Baltimore Cty, Dept Informat Syst, Baltimore, MD 21228 USA
来源：
JOURNAL OF MANAGEMENT INFORMATION SYSTEMS | 2016年 / 33卷 / 02期
基金：
美国国家科学基金会;
关键词：
deception detection; eWoM; electronic word of mouth; feature pruning; fake online reviews; online reviewer behavior; user-generated content; CONSUMER REVIEWS; PRODUCT REVIEWS; DECEPTION; IMPACT;
D O I：
10.1080/07421222.2016.1205907
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The value and credibility of online consumer reviews are compromised by significantly increasing yet difficult-to-identify 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 model without sacrificing its performance.
引用
收藏
页码：456 / 481
页数：26
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