Towards the development of an explainable e-commerce fake review index: An attribute analytics approach

Das, Ronnie; Ahmed, Wasim; Sharma, Kshitij; Hardey, Mariann; Dwivedi, Yogesh K.; Zhang, Ziqi; Apostolidis, Chrysostomos; Filieri, Raffaele

Abstract

Instruments of corporate risk and reputation assessment tools are quintessentially developed on structured quantitative data linked to financial ratios and macroeconomics. An emerging stream of studies has challenged this norm by demonstrating improved risk assessment and model prediction capabilities through unstructured textual corporate data. Fake online consumer reviews pose serious threats to a business’ competitiveness and sales performance, directly impacting revenue, market share, brand reputation and even survivability. Research has shown that as little as three negative reviews can lead to a potential loss of 59.2 % of customers. Amazon, as the largest e-commerce retail platform, hosts over 85,000 small-to-medium-size (SME) retailers (UK), selling over fifty percent of Amazon products worldwide. Despite Amazon's best efforts, fake reviews are a growing problem causing financial and reputational damage at a scale never seen before. While large corporations are better equipped to handle these problems more efficiently, SMEs become the biggest victims of these scam tactics. Following the principles of attribute (AA) and responsible (RA) analytics, we present a novel hybrid method for indexing enterprise risk that we call the Fake Review Index (
). The proposed modular approach benefits from a combination of structured review metadata and semantic topic index derived from unstructured product reviews. We further apply LIME to develop a Confidence Score, demonstrating the importance of explainability and openness in contemporary analytics within the OR domain. Transparency, explainability and simplicity of our roadmap to a hybrid modular approach offers an attractive entry platform for practitioners and managers from the industry.

Citation

Das, R., Ahmed, W., Sharma, K., Hardey, M., Dwivedi, Y. K., Zhang, Z., Apostolidis, C., & Filieri, R. (2024). Towards the development of an explainable e-commerce fake review index: An attribute analytics approach. European journal of operational research, https://doi.org/10.1016/j.ejor.2024.03.008

• Journal Article Type: Article
• Acceptance Date: Mar 4, 2024
• Online Publication Date: Mar 11, 2024
• Publication Date: 2024
• Deposit Date: Mar 19, 2024
• Publicly Available Date: Mar 12, 2026
• Journal: European Journal of Operational Research
• Print ISSN: 0377-2217
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1016/j.ejor.2024.03.008
• Keywords: Information Systems and Management; Management Science and Operations Research; Modeling and Simulation; General Computer Science; Industrial and Manufacturing Engineering
• Public URL: https://hull-repository.worktribe.com/output/4608478