EconPapers    
Economics at your fingertips  
 

Review Spam Detection by Highlighting Potential Spammers and Diminishing Their Effect

Fatemeh Keshavarz, Ayeshaa Abdul Waheed, Btissam Rachdi and Reda Alhajj
Additional contact information
Fatemeh Keshavarz: University of Calgary, Canada
Ayeshaa Abdul Waheed: University of Calgary, Canada
Btissam Rachdi: University of Calgary, Canada
Reda Alhajj: University of Calgary, Canada and Global University, Lebanon

International Journal of E-Business Research (IJEBR), 2018, vol. 14, issue 1, 54-76

Abstract: Nowadays, millions of products and services are available to the public online. Therefore, searching for the best products which meets individuals' expectations would be difficult due to the existence of too many alternative choices. One of the most reliable approaches to choose a product or service is to exploit the experience of people who have already tried them, and are expected to have reported their almost honest opinions about them. A reviewing system is a place where individuals share their experience on products and services. Individuals may read and/or write their reviews which may be neutral and professional or biased. Moreover, companies utilize reviewing systems to apply opinion mining techniques in order to improve their goods or services and may be to watch their competitors. However, the popularity of reviewing systems ignites this motivation for some people to try to influence viewers by entering their fake reviews to promote some products or defame some others. These spam reviews should be detected and eliminated to prevent misleading potential customers and unethically affect the market. Opinion mining should be adapted to locate and eliminate potential spam reviews. In this paper, some review spam detection approaches have been proposed and examined over a sample dataset. The proposed approaches consider patterns that existed in trends of reviews, as well as reviewers' behavior. The approaches depend on various strategies such as observing abnormal trends, detecting uncommon or suspicious behaviors, investigating group activities, among others. The reported test results revealed some promising outcome.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJEBR.2018010104 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jebr00:v:14:y:2018:i:1:p:54-76

Access Statistics for this article

International Journal of E-Business Research (IJEBR) is currently edited by Mohammad Nabil Almunawar

More articles in International Journal of E-Business Research (IJEBR) from IGI Global
Bibliographic data for series maintained by Journal Editor ().

 
Page updated 2025-03-19
Handle: RePEc:igg:jebr00:v:14:y:2018:i:1:p:54-76