A Weakly Supervised MIL Approach to Fake News Detection via Propagation Tree Analysis

This paper presents a weakly supervised Multiple Instance Learning (MIL) framework for fake news detection in social media, leveraging propagation tree analysis to model the spread of misinformation across online networks. Unlike traditional text-based or graph-based methods, our approach captures fine-grained post-level stances (support, denial, question, comment) and aggregates them to infer news veracity using a novel hierarchical attention mechanism. The framework in-corporates social network dynamics of information diffusion, offering deeper insights into how user interactions amplify or suppress misinformation. We evaluate our model on benchmark datasets, including PolitiFact and GossipCop from FakeNewsNet, comprising over 23,000 news articles and hundreds of thousands of user engagements, as well as on the SemEval-8 dataset for binary classification of true vs. fake news. Our method achieves up to 94.3% accuracy and 91.7% F1-score, outperforming state-of-the-art machine learning and deep learning baselines. Ablation studies further validate the contribution of stance aggregation and attention-based propagation modeling. These results highlight the effectiveness of integrating stance detection, propagation structures, and weakly supervised learning for scalable and interpretable fake news verification in online environments.