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.

Knowledge graphs can effectively organize and represent information related to emergency resources for unforeseen sudden events. In this study, we construct a model layer for the knowledge graph of emergency resources, focused on sudden events, through the classification and analysis of unforeseen disaster measures. This study defines eight interconnected entity types, each characterised by a set of attributes and engaging in one or more relationships with other entity types. Utilizing 121 incident investigation reports from the emergency management departments of various provinces and cities over the past five years, we select five entities with the highest frequency of occurrence along with their corresponding four relationships. We then design an extraction plan for these entities and relationships. Based on the completed knowledge graph data, we formulate 14 questions related to emergency resources for sudden events and construct 19 corresponding question-and-answer templates using a template-based question-answering (QA) approach. We retrieve the corresponding Cypher statement templates through template mapping and obtain the question answers through querying. Finally, we design a knowledge graph question-and-answer system using the Django web framework, which includes entity queries and knowledge QA functions, specifically for emergency resources related to sudden events.

• We propose a hybrid model, ECR-BERT, for explainable sentiment analysis. • We infer emotion-cognitive knowledge based on the OCC model. • We propose a self-adaptive fusion algorithm to mitigate the knowledge noise problem. • We adopt knowledge-enabled feature representation to efficiently utilize knowledge. • Evaluation on four real-world Weibo datasets shows the efficacy of our method. Sentiment analysis of online public opinions on emergencies (OPOEs) requires accurate and explainable results to facilitate a better understanding of public sentiment and effective crisis management, but it is challenging due to the complexity and diversity of emotions contained in OPOEs. In this paper, we propose an Emotion-Cognitive Reasoning integrated BERT (ECR-BERT) for sentiment analysis of OPOEs. ECR-BERT combines an emotion model and deep learning to provide reliable auxiliary knowledge to improve BERT. Specifically, we use the emotion model proposed by Ortony, Clore, and Collins (OCC) to build emotion-cognitive rules and perform emotion-cognitive reasoning to discover emotion-cognitive knowledge. To mitigate the impact of knowledge noise, we propose a novel self-adaptive fusion algorithm that provides a selection mechanism for the incorporation of knowledge. In addition, we utilize knowledge-enabled feature representation to efficiently exploit inferred knowledge. Our evaluation on four real-world OPOE datasets shows that ECR-BERT significantly outperforms other BERT-based models, achieving state-of-the-art results with an absolute average accuracy improvement of 0.82%, 1.74%, 0.98%, and 1.37% over BERT, respectively. In addition, ECR-BERT provides a detailed explanation of how sentiment polarity is derived from fine-grained emotion categories. The ablation study demonstrates the effectiveness of each technique. In conclusion, ECR-BERT is an excellent choice for sentiment analysis of OPOEs, providing accurate and explainable results for crisis management.