STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Mobile Computing, Communications & Mobile Networks Original Research

AI- based Prediction of Misinformation Virality Before Wide Dissemination using Attention-based Multi-modal

  1. Shokooh Khandan Department of computing and mathematics, Manchester Metropolitan University
  2. Deniz Beyazgul Department of computing and mathematics, Manchester Metropolitan University

Abstract

Misinformation on social media has emerged as a critical global challenge, impacting public health, democratic institutions, and societal trust. While existing research has largely concentrated on detecting misinformation after it begins circulating, predicting its virality before wide dissemination remains an underexplored area, limited work addresses predicting its virality before wide dissemination. This paper presents a conceptual framework using attention-based multi-modal deep learning models to estimate the virality of misinformation posts at the point of publication. By integrating textual, visual, and metadata features with attention mechanisms, the proposed system aims to identify potentially viral misinformation proactively. Although this work is theoretical, it lays the groundwork for future implementation and evaluation of such systems, highlighting key components, potential challenges, and research directions. We argue that pre-dissemination prediction represents a paradigm shift from reactive moderation to proactive mitigation, which could help reduce the societal harm caused by viral falsehoods.

Keywords

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