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

• Published in: European journal of operational research 2024-09, Vol.317 (2), p.382-400
• Main Authors: Das, Ronnie, Ahmed, Wasim, Sharma, Kshitij, Hardey, Mariann, Dwivedi, Yogesh K., Zhang, Ziqi, Apostolidis, Chrysostomos, Filieri, Raffaele
• Format: Article
• Language: English
• Subjects: AI explainability; Amazon; BERT; Business administration; Fake reviews; Humanities and Social Sciences; LIME confidence score; Risk analysis; Topic model indexing
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2024.03.008

•E-commerce fake reviews tend to have higher ratings and shorter lengths compared to original reviews.•Ensembled methods combining structured and unstructured data increases LSTM model prediction performance.•Semantic analysis and indexing of fake reviews help to improve LSTM model performance and explainability.•Attribute analytics will lead the way to better managerial adoption of fake review indexing method. 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 (RFRI). 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.