Mobile Health Text Misinformation: A Data Mining-Based Detection Model

Abstract

In the era of digital communication, mobile health (mHealth) platforms have significantly contributed to the dissemination of health-related information, offering users easy access to medical advice, reminders, and consultations. However, the proliferation of misinformation in these platforms has raised concerns due to its potential harm to public health. This paper proposes a data mining-based model for detecting mobile health text misinformation by analyzing healthrelated text data for inconsistencies, misleading claims, and false information. Using advanced techniques in natural language processing (NLP), machine learning, and deep learning, the model aims to identify and classify misleading health content efficiently. The proposed system applies a combination of supervised and unsupervised learning techniques to recognize patterns, validate health claims, and alert users to potentially harmful misinformation. The paper concludes by discussing the implications of such a detection system in improving the reliability of mHealth content and preventing health-related misinformation from reaching vulnerable populations.