Datasets: fake news multimodal datasets (Twitter and Weibo). Credit: Data 1: (Twitter dataset): The data that support the findings of this study are derived from “Detection and visualization of misleading content on Twitter” at https://github.com/MKLab-ITI/image-verification-corpus, DOI: "10.1007/s13735-017-0143-x." Data 2: (Weibo dataset): The data that support the findings of this study are derived from “EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection” at https://github.com/yaqingwang/EANN-KDD18?tab=readme-ov-file, DOI: “10.1145/3219819.3219903.”

This study proposes an innovative approach for multimodal fake news detection that utilizes a stick-breaking smoothed Dirichlet distribution. This approach enables the model to capture intricate, subtle interactions between modalities more effectively, thereby improving detection performance and enhancing the system's adaptability to various forms of fake news content