Early Detection of Mental Health Crises through Social Media Analysis

Mental health crises represent a significant global public health challenge, with early detection crucial for effective intervention. This study leverages natural language processing (NLP) to identify subtle linguistic markers on social media that may signal deteriorating mental health before clinical symptoms become apparent. We analyzed 12 months of social media posts from 10,000 users who self-identified with mental health conditions and an equal-sized control group. Our novel BERT- LSTM hybrid model achieved 83% accuracy in identifying users experiencing mental health decline. Key linguistic predictors included increased use of first-person singular pronouns, neg- ative emotion words, absolutist language, and altered posting patterns, particularly late-night activity. Significantly, our model detected concerning changes an average of 6.3 weeks before users explicitly mentioned mental health problems, creating a critical intervention window. These findings demonstrate the potential of NLP-based social media analysis as an early warning system for mental health crises, while raising important considerations about privacy, consent, and ethical implementation of such technologies in clinical settings.