Deep Learning–Based Monitoring of Self-Harm Indicators Across Online Social Communities

Abstract

Self-harm is a rising global public health concern, and early detection of psychological distress within online communities can play a crucial role in prevention and timely intervention [1], [4]. With the rapid increase in social media usage, individuals experiencing emotional instability often express their feelings through text posts, comments, hashtags, and multimedia interactions, creating digital footprints that may reflect underlying self-harm ideation [2], [8]. However, manual monitoring of self-harm–related discourse across large-scale social networks is impractical due to the vast volume of data, rapid information diffusion, linguistic ambiguity, and privacy-preserving user anonymity [17], [19]. To address these challenges, this research introduces a deep learning–based monitoring framework designed to automatically detect and track self-harm indicators across online social platforms [1], [5]. The proposed system utilizes pretrained language models and transformer-driven architectures to extract psychological cues from user-generated content, while temporal emotional modeling captures variations in sentiment, intent, and behavioral patterns over time [3], [6], [13]. The integration of multimodal features—including text sentiment, social engagement signals, and interaction patterns—enables accurate forecasting of emerging self-harm trends at the community and population levels [2], [9], [14]. Experimental results on real-world social media datasets demonstrate that the framework surpasses traditional machine-learning baselines in precision, recall, and early-warning sensitivity [5], [7], [10]. By offering continuous and large-scale behavioral surveillance, the proposed solution supports mental-health organizations, policy makers, and crisis-response teams in identifying high-risk groups and designing proactive interventions to mitigate self-harm incidents [11], [15], [20].