Towards a Review-Analytics-as-a-Service (RAaaS) Framework for SMEs: A Case Study on Review Fraud Detection and Understanding

Abstract

With the advancement of internet technology, customers increasingly rely on online reviews as a valuable source of information. The study aims to develop a marketing data analytics framework to manage online reviews, especially fake reviews, which have become a significant issue undermining the creditability of online review systems. As small and medium-sized enterprises often lack the capabilities to automatically derive customer insights from online reviews, this study proposes a cost-effective, extensible Review-Analytics-as-a-Service (RAaaS) framework that can be operated by non-data specialists to facilitate online review data analytics. We demonstrate the framework’s application by using two datasets with more than 400,000 online reviews from Yelp to simulate live platforms and demonstrate an analytic flow of review fraud detection and understanding. The findings reveal insights into the influence of fake reviews on product ranking and exposure rate. Moreover, it was found that there was a higher concentration of sadness and anger in fake reviews (vs. organic reviews). In addition, fake reviews tend to be shorter, more extreme (with the use of strong adverbs), and have different patterns of topic distribution. This study has important implications for different stakeholder groups including, but not limited to, SMEs, review platforms and customers.

Evaluating The Impact of Online Product Review Credibility and Online Product Review Quality on Purchase Intention of Online Consumers

“…These reviews serve as a crucial resource for potential customers, aiding them in making informed decisions based on the experiences and opinions shared by others. The significance of online product reviews in influencing consumer decisions is further supported by research conducted by Chau et al (2023) and Viglia et al (2016), who highlight that consumers often rely on external sources, such as online customer reviews, due to limited access to direct information about the quality of services offered. As articulated by Alrubaian et al (2019) and Erkan and Evans (2016), the perception of credibility revolves around individuals' beliefs regarding the accuracy and validity of evaluations.…”

Utilizing data analytics for fraud detection in accounting: A review and case studies

“…The study by Chau et al (2023) presents a case study on review fraud detection and understanding. This case study highlights the development of a cost-effective, extensible RAaaS framework that can be operated by non-data specialists.…”