Unveiling the Priorities and Challenges Involved With Artificial-Intelligence-Powered Fake News Detection: A Comprehensive Analysis

Abstract

In todays digital era, the proliferation of false news distribution through social media presents a serious threat to public opinion and societal development. In the present investigation, the challenges that are involved when using artificial intelligence/machine learning technologies in detecting false news are identified through a literature review, and 12 challenges are shortlisted based on the inputs from subject matter experts of the domain. This research employed the Analytic Hierarchy Process (AHP) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) to methodically analyze these challenges. The AHP is used for ranking the challenges based on their relative significance, and DEMATEL is used for categorizing them into causal and effect clusters. The results of the study highlighted that real-time challenges and evolving tactics are the most significant ones. Finally, the social, academic, and managerial implications of this study are offered. © 1999-2012 IEEE.