A Novel Comprehensive Framework for Detecting and Understanding Health-Related Misinformation

Abstract

The spread of health-related misinformation has become a significant global challenge, particularly during the COVID-19 pandemic. This study introduces a comprehensive framework for detecting and analyzing misinformation using advanced natural language processing techniques. The proposed classification model combines BERT embeddings with Bi-LSTM architecture and attention mechanisms, achieving high performance, including 99.47% accuracy and an F1-score of 0.9947. In addition to classification, topic modeling is employed to identify thematic clusters, providing valuable insights into misinformation narratives. The findings demonstrate the effectiveness and reliability of the proposed methodology in detecting misinformation while offering tools for understanding its underlying themes. The adaptable and scalable approach makes it applicable to various domains and datasets. This research improves public health communication and combating misinformation in digital environments.