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Towards the development of an explainable e-commerce fake review index: An attribute analytics approach
European Journal of Operational Research, Volume: 317, Issue: 2, Pages: 382 - 400
Swansea University Author:
Yogesh Dwivedi
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DOI (Published version): 10.1016/j.ejor.2024.03.008
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 capabiliti...
Published in: | European Journal of Operational Research |
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ISSN: | 0377-2217 |
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Elsevier BV
2024
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URI: | https://cronfa.swan.ac.uk/Record/cronfa65780 |
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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. 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v2 65780 2024-03-06 Towards the development of an explainable e-commerce fake review index: An attribute analytics approach d154596e71b99ad1285563c8fdd373d7 0000-0002-5547-9990 Yogesh Dwivedi Yogesh Dwivedi true false 2024-03-06 CBAE 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. Journal Article European Journal of Operational Research 317 2 382 400 Elsevier BV 0377-2217 Fake reviews; Amazon; Risk analysis; AI explainability; BERT; Topic model indexing; LIME confidence score 1 9 2024 2024-09-01 10.1016/j.ejor.2024.03.008 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University Another institution paid the OA fee 2024-06-06T16:48:38.8859787 2024-03-06T14:21:41.2310121 Faculty of Humanities and Social Sciences School of Management - Business Management Ronnie Das 1 Wasim Ahmed 0000-0001-8923-1865 2 Kshitij Sharma 0000-0003-3364-637x 3 Mariann Hardey 0000-0002-1027-0165 4 Yogesh Dwivedi 0000-0002-5547-9990 5 Ziqi Zhang 0000-0002-8587-8618 6 Chrysostomos Apostolidis 0000-0002-9613-880x 7 Raffaele Filieri 0000-0002-3534-8547 8 65780__30559__78eeea1114994f5b9b95699424a39cb0.pdf 65780.VoR.pdf 2024-06-06T16:46:59.9129432 Output 9497900 application/pdf Version of Record true © 2024 The Authors. This is an open access article under the CC BY license. true eng http://creativecommons.org/licenses/by/4.0/ |
title |
Towards the development of an explainable e-commerce fake review index: An attribute analytics approach |
spellingShingle |
Towards the development of an explainable e-commerce fake review index: An attribute analytics approach Yogesh Dwivedi |
title_short |
Towards the development of an explainable e-commerce fake review index: An attribute analytics approach |
title_full |
Towards the development of an explainable e-commerce fake review index: An attribute analytics approach |
title_fullStr |
Towards the development of an explainable e-commerce fake review index: An attribute analytics approach |
title_full_unstemmed |
Towards the development of an explainable e-commerce fake review index: An attribute analytics approach |
title_sort |
Towards the development of an explainable e-commerce fake review index: An attribute analytics approach |
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d154596e71b99ad1285563c8fdd373d7 |
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d154596e71b99ad1285563c8fdd373d7_***_Yogesh Dwivedi |
author |
Yogesh Dwivedi |
author2 |
Ronnie Das Wasim Ahmed Kshitij Sharma Mariann Hardey Yogesh Dwivedi Ziqi Zhang Chrysostomos Apostolidis Raffaele Filieri |
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European Journal of Operational Research |
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317 |
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382 |
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2024 |
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Swansea University |
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10.1016/j.ejor.2024.03.008 |
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Elsevier BV |
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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. |
published_date |
2024-09-01T16:48:38Z |
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1801127375579119616 |
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11.021648 |