Health Misinformation Detection Using AI-Based Techniques: Comparative Analysis

In an era marked by the rapid dissemination of information through digital platforms, health misinformation poses a significant threat to public well-being. This paper addresses the critical issue of health misinformation detection by leveraging the power of machine and deep learning techniques. With the potential to curb the harmful effects of false health information, our study delves into the development and evaluation of automated systems for detecting misleading or inaccurate health-related content. This paper provides insights into the evolving landscape of health misinformation and the inherent challenges associated with its identification. We discuss the process of data collection, preprocessing, and feature engineering, emphasizing the importance of creating balanced datasets that reflect the complexity of misinformation in the health domain. Our research encompasses both sophisticated deep learning architectures and conventional machine learning models. We introduce a variety of algorithms and thoroughly assess their performance using industry-standard metrics including accuracy, precision, recall, F1-score, and ROC-AUC. The algorithms vary from logistic regression to cutting-edge neural networks. Our experiment’s positive results point to a potential way to lessen the negative consequences of health misinformation on the public's health: automated detection of misinformation. We hope to contribute to the continuing efforts to stop the spread of false information about health in the digital era by having talks about the limits of our study and potential paths for future research. The aforementioned study highlights the importance of utilizing machine learning and deep learning techniques to protect public health by detecting and mitigating the widespread danger of health disinformation.