Misinformation verification on social media: Bibliometric insights and a human–ai hybrid framework

Abstract

The rapid proliferation of misinformation on social media has intensified scholarly efforts to develop artificial intelligence–based detection systems, increasingly framing verification as a computational task centred on algorithmic performance and model optimisation. While these technological advances significantly improve scalability and detection efficiency, they also risk marginalising human judgement, ethical accountability, and contextual interpretation in the verification process. This study critically examines the epistemic orientation of contemporary misinformation verification research and proposes a hybrid framework that rebalances technological automation with human-centred evaluation. Using a bibliometric analysis of 54 Scopus-indexed publications, this study maps the intellectual structure and thematic evolution of misinformation verification scholarship between 2022 and 2025. Network and overlay analyses reveal a strong concentration of research around algorithmic modelling, detection performance, and transformer-based architectures, alongside a relative absence of explicit normative verification frameworks within dominant research clusters. These findings indicate an emerging automation bias in the field, where artificial intelligence increasingly functions as the primary epistemic authority in assessing informational credibility. In response to this imbalance, the article introduces the Human–AI Hybrid Verification Framework (HAVF), which conceptualises misinformation verification as a layered process integrating automated detection with structured human evaluation. Drawing on the epistemic principle of tabayyun, the framework emphasises deliberate scrutiny, contextual assessment, and ethical responsibility as essential complements to AI-based detection systems. By situating verification at the intersection of artificial intelligence, information ethics, and digital governance, this study contributes a hybrid epistemic perspective for addressing misinformation in contemporary social media ecosystems.