A fake review identification framework considering the suspicion degree of reviews with time burst characteristics. (15th March 2022)

• Record Type: Journal Article
• Authors: Wang, Ning; Yang, Jun; Kong, Xuefeng; Gao, Ying
• Is Part Of: Expert systems with applications. Volume 190(2022)
• Publication Date: 2022-03-15
• Subjects: Fake review -- Time series -- Machine learning -- Data and text mining -- Doc2vec; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.116207
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Time burst characteristic is studied by 3 aspects in fake review identification. A suspicion degree method of fake reviews is proposed from (1) by LOF algorithm. A fake review identification framework is given including the suspicion degree. The case study shows that the proposed method outperforms the existed methods. Abstract: With the rapid development of e-commerce, online reviews have played an increasingly important role in consumers' shopping intentions and behaviors. Therefore, how to effectively identify fake reviews has become one of the important issues that need to be resolved. Since the existing methods do not fully consider the time burst characteristics of reviews, this paper proposes a suspicion degree determining method based on the three-dimensional time series. Besides, combining the suspicion degree feature, review text features, and reviewer's behavior features together, this paper proposes a more comprehensive fake review identification framework. The yelp and amazon public data sets are carried out to verify the effectiveness of the proposed method, and the experimental results show that the proposed method outperforms the most advanced methods.