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.

Federated learning is a machine learning framework that facilitates training a shared model from distributed clients. However, challenges persist in optimizing communication efficiency. In this paper, we focus on hierarchical federated learning and model its global aggregation as a network function computation problem, where the central server desires to compute the arithmetic sum of the clients' gradients. Inspired by network coding, we propose two Coded Hierarchical Federated Learning (CHFL) approaches to enhance communication efficiency. The first approach, Separated CHFL (S-CHFL), involves transmitting divided segments separately to relays using a greedy algorithm. We establish the upper and lower bounds of the computing rate, showing that S-CHFL can achieve perfect balance in reverse combination network and reach the upper bound in certain networks. The second approach is Mixed CHFL (M-CHFL) where divided segments are mixed into linear combinations for transmission. We show that M-CHFL may be more efficient when data comes from a sufficiently large alphabet and analyze its upper bound for the computing rate.