Towards Real-Time Misinformation Detection in Mobile Health Texts Using Adaptive Systems

Abstract

With the increasing adoption of mobile health (mHealth) technologies, the dissemination of health information through mobile platforms has grown exponentially. However, the rapid spread of misinformation in mHealth texts has raised concerns about its potential negative impacts on individuals' health decisions and outcomes. This paper explores the development of adaptive systems for real-time misinformation detection in mHealth texts. We emphasize the importance of identifying and mitigating misinformation in such texts by focusing on system adaptability and real-time performance. The study identifies the challenges associated with detecting misinformation in the diverse and informal language of mobile health communications, while proposing a framework for real-time misinformation detection that dynamically adapts to evolving content. Furthermore, it suggests how adaptive machine learning models can improve the accuracy and efficiency of misinformation detection in this rapidly changing domain. Our findings indicate that by leveraging real-time analysis and adaptability, mobile health platforms can mitigate the risks associated with misinformation, ensuring the dissemination of accurate, reliable health information to users.