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

本研究提出了一种用于多模态假新闻检测的创新方法，该方法采用折断式平滑狄利克雷分布（stick-breaking smoothed Dirichlet distribution）。此方法可使模型更有效地捕捉模态间复杂且微妙的交互关系，进而提升检测性能，并增强系统对各类假新闻内容的适配能力。