Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions
Journal article
Akhtar, P., Ghouri, A., Khan, H.U.R., Amin ul Haq, M., Awan, U., Zahoor, N., Khan, Z. and Ashraf, A. (2022). Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions. Annals of Operations Research. 327, p. 633–657. https://doi.org/10.1007/s10479-022-05015-5
Authors | Akhtar, P., Ghouri, A., Khan, H.U.R., Amin ul Haq, M., Awan, U., Zahoor, N., Khan, Z. and Ashraf, A. |
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Abstract | Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies based on data collected from Indonesia, Malaysia, and Pakistan, we developed a FNaD detection model aimed at preventing SCDs. This model based on multiple data sources has shown evidence of its effectiveness in managerial decision-making. Our study further contributes to the supply chain and AI-ML literature, provides practical insights, and points to future research directions. |
Year | 2022 |
Journal | Annals of Operations Research |
Journal citation | 327, p. 633–657 |
Publisher | Springer |
ISSN | 1572-9338 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s10479-022-05015-5 |
Web address (URL) | http://www.scopus.com/inward/record.url?eid=2-s2.0-85143833590&partnerID=MN8TOARS |
Publication dates | |
Online | 01 Nov 2022 |
Publication process dates | |
Accepted | 27 Sep 2022 |
Deposited | 13 Feb 2023 |
Publisher's version | License File Access Level Open |
https://openresearch.lsbu.ac.uk/item/933vz
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