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

Author

Listed:
  • Pervaiz Akhtar

    (University of Aberdeen Business School, University of Aberdeen, King’s College
    Imperial College London)

  • Arsalan Mujahid Ghouri

    (Universiti Pendidikan Sultan Idris)

  • Haseeb Ur Rehman Khan

    (Universiti Pendidikan Sultan Idris)

  • Mirza Amin ul Haq

    (Iqra University)

  • Usama Awan

    (Inland Norway University of Applied Sciences)

  • Nadia Zahoor

    (Queen Mary University of London)

  • Zaheer Khan

    (University of Aberdeen Business School, University of Aberdeen, King’s College
    University of Vaasa)

  • Aniqa Ashraf

    (University of Science and Technology of China)

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.

Suggested Citation

  • Pervaiz Akhtar & Arsalan Mujahid Ghouri & Haseeb Ur Rehman Khan & Mirza Amin ul Haq & Usama Awan & Nadia Zahoor & Zaheer Khan & Aniqa Ashraf, 2023. "Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions," Annals of Operations Research, Springer, vol. 327(2), pages 633-657, August.
  • Handle: RePEc:spr:annopr:v:327:y:2023:i:2:d:10.1007_s10479-022-05015-5
    DOI: 10.1007/s10479-022-05015-5
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