Author: Aryan Manna, Aarzoo, Dr. Sundeep Katevarapu
Abstract
Background: The global fact-checking ecosystem has expanded from 44 organizations in 2014 to over 400 by 2024, yet confronts fundamental challenges to both effectiveness and sustainability. Generative AI has dramatically widened the production asymmetry between misinformation generation and human verification capacity. The continued influence effect documented by Lewandowsky et al. (2012) constrains correction durability, while platform funding dependency creates institutional vulnerabilities.
Objectives: To synthesize experimental evidence on fact-checking effectiveness through meta-analysis, analyze organizational models and sustainability challenges through cross-national case studies, and propose a Sustainable Fact-Checking Framework addressing the ecosystem’s structural vulnerabilities.
Methods: Two-component design integrating meta-analytic synthesis of 28 experimental studies with 42 effect sizes and approximately 34,000 participants following PRISMA guidelines, with cross-national organizational case study analysis of 24 fact-checking operations across six continents. Meta-analysis used random-effects models with moderator analyses. Case studies employed semi-structured interviews and organizational document analysis.
Results: Meta-analysis found weighted average effect size d=0.31 (95% CI: 0.24-0.38) for fact-check corrections. Effects were moderated by format (video d=0.42 vs. text d=0.26), topic (health d=0.38 vs. political d=0.24), and timing (immediate d=0.39 vs. delayed d=0.21). Case study analysis identified five sustainability challenges: platform funding dependency (67% of organizations deriving over 40% revenue from platforms), audience reach limitations, adversarial adaptation, scalability constraints, and political instrumentalization.
Conclusion: Fact-checking is necessary but not sufficient as a misinformation response. The Sustainable Fact-Checking Framework addresses structural vulnerabilities through funding diversification, collaborative verification networks, AI-assisted scalability, and strategic integration with complementary prebunking and media literacy approaches.
Keywords: fact-checking, misinformation, information integrity, media trust, verification, credibility, IFCN, digital literacy, correction effects, sustainability.
