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
AuthorsAkhtar, P., Ghouri, A., Khan, H.U.R., Amin ul Haq, M., Awan, U., Zahoor, N., Khan, Z. and Ashraf, A.
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.

Year2022
JournalAnnals of Operations Research
Journal citation327, p. 633–657
PublisherSpringer
ISSN1572-9338
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
Online01 Nov 2022
Publication process dates
Accepted27 Sep 2022
Deposited13 Feb 2023
Publisher's version
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Open
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