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

IF 4 Q2 BUSINESS

Australasian Marketing Journal Pub Date : 2023-02-10 DOI:10.1177/14413582221146004

Xuan Truong Du Chau, Thanh Toan Nguyen, Vinh Khiem Tran, S. Quach, Park Thaichon, Jun Jo, Bay Vo, Quang Dieu Tran, Quoc Viet Hung Nguyen

{"title":"Towards a Review-Analytics-as-a-Service (RAaaS) Framework for SMEs: A Case Study on Review Fraud Detection and Understanding","authors":"Xuan Truong Du Chau, Thanh Toan Nguyen, Vinh Khiem Tran, S. Quach, Park Thaichon, Jun Jo, Bay Vo, Quang Dieu Tran, Quoc Viet Hung Nguyen","doi":"10.1177/14413582221146004","DOIUrl":null,"url":null,"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.","PeriodicalId":47402,"journal":{"name":"Australasian Marketing Journal","volume":" ","pages":""},"PeriodicalIF":4.0000,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Australasian Marketing Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/14413582221146004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS","Score":null,"Total":0}

引用次数: 0

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.

查看原文本刊更多论文

面向中小企业的审核-分析-服务(RAaaS)框架:审核欺诈检测与理解的案例研究

随着互联网技术的进步，顾客越来越依赖在线评论作为有价值的信息来源。该研究旨在开发一个营销数据分析框架，以管理在线评论，特别是虚假评论，这已经成为破坏在线评论系统可信度的一个重要问题。由于中小型企业通常缺乏从在线评论中自动获取客户见解的能力，本研究提出了一个具有成本效益、可扩展的评论分析即服务(RAaaS)框架，该框架可由非数据专家操作，以促进在线评论数据分析。我们通过使用两个来自Yelp的超过40万条在线评论的数据集来模拟现场平台，并演示了评论欺诈检测和理解的分析流程，从而演示了该框架的应用。研究结果揭示了虚假评论对产品排名和曝光率的影响。此外，我们还发现，在虚假评论中(与自然评论相比)，悲伤和愤怒的情绪更为集中。此外，虚假评论往往更短，更极端(使用强副词)，并且具有不同的主题分布模式。本研究对不同的利益相关者群体具有重要的启示意义，包括但不限于中小企业、评论平台和客户。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Australasian Marketing Journal BUSINESS-

CiteScore

14.90

自引率

16.70%

发文量

期刊介绍： The Australasian Marketing Journal (AMJ) is the official journal of the Australian and New Zealand Marketing Academy (ANZMAC). It is an academic journal for the dissemination of leading studies in marketing, for researchers, students, educators, scholars, and practitioners. The objective of the AMJ is to publish articles that enrich and contribute to the advancement of the discipline and the practice of marketing. Therefore, manuscripts accepted for publication will be theoretically sound, offer significant research findings and insights, and suggest meaningful implications and recommendations. Articles reporting original empirical research should include defensible methodology and findings consistent with rigorous academic standards.