Developing Self-Reconfigurable Models for Detecting Health Text Misinformation

Abstract

The rapid dissemination of health-related misinformation on digital platforms poses significant risks to public health, particularly when individuals make medical decisions based on false or misleading information. Recent advancements in artificial intelligence (AI) and natural language processing (NLP) have paved the way for automated systems to identify and counter misinformation. However, the challenge lies in developing robust models that are adaptable to various health misinformation topics, maintain high accuracy across multiple platforms, and evolve over time as misinformation tactics change. This paper explores the development of selfreconfigurable models capable of detecting health-related misinformation, focusing on their adaptability, accuracy, and scalability. The study emphasizes the importance of leveraging AIdriven techniques to build systems that can dynamically adjust to new forms of misinformation. This research aims to propose a framework that integrates continuous learning, data diversity, and reconfigurable model architectures to create scalable solutions for health misinformation detection.