Credibility Analysis for Online Product Reviews

Min Chen and Anusha Prabakaran
Additional contact information
Min Chen: University of Washington Bothell, USA
Anusha Prabakaran: University of Washington Bothell, USA

International Journal of Multimedia Data Engineering and Management (IJMDEM), 2018, vol. 9, issue 3, 37-54

Abstract: With the prevalence of e-commerce, online product reviews are increasingly considered crowd-sourced consumer opinions that significantly influence customer purchasing decisions and product rankings. It is therefore important to ensure the truthfulness of reviews by detecting and filtering out fake/spam reviews. This article presents an effective framework to analyze review credibility for spam detection and opinion mining. It incorporates three methods: duplicated review detection, anomaly detection, and incentivized review detection, that complement each other to produce statistical credibility scores indicating review credibility. A practical end-to-end system is designed and developed accordingly, and is equipped with high-level data visualization for easy interpretation and summarization of the analysis results. Experiments on an Amazon review dataset demonstrate its efficiency, scalability and accuracy. This system could help e-commerce and consumers identify fake reviews, refine product rankings, and constrain vendors and spammers from engaging in dishonest practices.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
https://services.igi-global.com/resolvedoi/resolve ... 18/IJMDEM.2018070103 (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:jmdem0:v:9:y:2018:i:3:p:37-54

Access Statistics for this article

International Journal of Multimedia Data Engineering and Management (IJMDEM) is currently edited by Shu-Ching Chen

More articles in International Journal of Multimedia Data Engineering and Management (IJMDEM) from IGI Global Scientific Publishing
Bibliographic data for series maintained by Journal Editor ().