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

Pervaiz Akhtar; Arsalan Mujahid Ghouri; Haseeb Ur Rehman Khan; Mirza Amin Ul Haq; Usama Awan; Nadia Zahoor; Zaheer Khan; Aniqa Ashraf

doi:10.1007/s10479-022-05015-5

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

Ann Oper Res. 2022 Nov 1:1-25. doi: 10.1007/s10479-022-05015-5. Online ahead of print.

Authors

Pervaiz Akhtar^{1

2}, Arsalan Mujahid Ghouri³, Haseeb Ur Rehman Khan⁴, Mirza Amin Ul Haq⁵, Usama Awan⁶, Nadia Zahoor⁷, Zaheer Khan^{1

8}, Aniqa Ashraf⁹

Affiliations

¹ University of Aberdeen Business School, University of Aberdeen, King's College, AB24 5UA Aberdeen, UK.
² Imperial College London, SW7 2BU London, UK.
³ Faculty of Management and Economics, Universiti Pendidikan Sultan Idris, Tanjong Malim, Malaysia.
⁴ Faculty of Art, Computing, and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjong Malim, Malaysia.
⁵ Department of Business Administration, Iqra University, Karachi, Pakistan.
⁶ Department of Business Administration, Inland School of Business and Social Sciences, Inland Norway University of Applied Sciences, Hamar, Norway.
⁷ School of Business and Management, Queen Mary University of London, London, UK.
⁸ Innolab, University of Vaasa, Vaasa, Finland.
⁹ CAS-Key Laboratory of Crust-Mantle Materials and the Environments, School of Earth and Space Sciences, University of Science and Technology of China, 230026 Hefei, PR 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.

Keywords: Artificial intelligence; Disinformation; Effective decision making; Fake news; Machine learning; Misinformation; Supply chain disruptions.

Publication types

News