FORECASTING NATIONAL-LEVEL SELF-HARM TRENDS WITH SOCIAL NETWORKS

Abstract

“Forecasting National-Level Self-Harm Trends with Social Networks” presents an intelligent datadriven framework that analyzes large-scale social network activity to identify patterns associated with mental health concerns and predict national-level self-harm trends. The study utilizes machine learning, natural language processing, and sentiment analysis techniques to examine publicly available social media content, behavioral indicators, and temporal trends while maintaining user privacy and ethical standards. By integrating historical health statistics with online social interaction data, the proposed system aims to provide early warning signals for mental health authorities and policymakers to support timely intervention strategies and resource allocation. Experimental results demonstrate that social network signals can significantly improve forecasting accuracy compared to traditional statistical methods, enabling proactive public health planning and enhancing mental health awareness at a national scale.