Enhancing Interpretability: A Hierarchical Belief Rule-Based (HBRB) Method for Assessing Multimodal Social Media Credibility

Abstract

User and artificial intelligence generated contents, coupled with the multimodal nature of information, have made the identification of false news an arduous task. While models can assist users in improving their cognitive abilities, commonly used black-box models lack transparency, posing a significant challenge for interpretability. This study proposes a novel credibility assessment method of social media content, leveraging multimodal features by optimizing the hierarchical belief rule-based (HBRB) inference method. Compared to other popular feature engineering and deep learning models, our method integrates, analyses, and filters relevant features, improving the HBRB structure to make the model layered, independent, and interconnected, enhancing interpretability and controllability, thereby addressing the rule combination explosion problem. The results highlight the potential of our method to improve the integrity of the online information ecosystem, offering a promising solution for more transparent and reliable credibility assessment in social media.