Work

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

Year: 2025

Type: article

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.

Source: International Journal of Intelligent Systems

DOI: 10.1155/int/7184626

Authors: Peng Wu, Jiahong Lin, Zhiyuan Ma, Huiwen Li

Institutions: Nanjing University of Science and Technology, Nanjing Normal University

Language: English

Topic: Misinformation and Its Impacts

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $2,700

Funders: National Natural Science Foundation of China, Ministry of Science and Technology of the People's Republic of China