Insight-FakeNet: Explainable AI for Motive Analysis in News Dissemination and Youth Online Harm Mitigation

Understanding the motives behind news dissemination is essential in today's digital age to ensure media transparency and trust.While extensive research has focused on detecting fake news, limited attention has been given to the computational analysis of the underlying motives driving news propagation.This paper introduces Insight-FakeNet, an Explainable Artificial Intelligence (XAI) model that leverages Natural Language Processing (NLP) techniques to classify and interpret the motives behind news dissemination.Utilizing the MIND-Set dataset, a re-annotated version of widely used fake news datasets with motive-specific labels, Insight-FakeNet identifies motives such as political influence, financial gain, entertainment, propaganda, and policy-driven agendas.The model incorporates TF-IDF-based feature extraction and evaluates several tree ensemble methods, with LightGBM achieving an F1 score of 0.92, outperforming other approaches.To ensure interpretability, the model integrates Local Interpretable Model-agnostic Explanations (LIME), offering insights into key linguistic features that influence classification decisions.The findings demonstrate the model's capability to computationally analyse and explain diverse motives in news content, aiding stakeholders such as policymakers, youth online safety practitioners, journalists, and AI practitioners in fostering trust and accountability in media systems and mitigating online harms among young audiences.This research bridges gaps in motive analysis and contributes to advancing explainable AI applications in computational language processing, with direct implications for youth online harm mitigation.