Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions

Pervaiz Akhtar* (Corresponding Author), Arsalan Mujahid Ghouri, Haseeb Ur Rehman Khan, Mirza Amin ul Haq, Usama Awan, Nadia Zahoor, Zaheer Khan, Aniqa Ashraf

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)
9 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)633–657
Number of pages25
JournalAnnals Of Operations Research
Volume327
Early online date1 Nov 2022
DOIs
Publication statusPublished - 1 Aug 2023

Keywords

  • Fake news
  • Disinformation
  • misinformation
  • Artificial intelligence
  • Machine learning
  • Supply chain disruptions
  • Effective decision making

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