Reliability of Machine Learning Models in Detecting Suicidal Behavior from Text-Based Data

Suicide is among the most pressing public health challenges globally, claiming approximately 727,000 lives each year. As digital communication has become a primary medium for human expression, text-based data from social media platforms, clinical records, and online communities increasingly serves as a valuable resource for identifying individuals at risk. Machine learning (ML) and natural language processing (NLP) have rapidly emerged as promising tools in this domain, offering the possibility of automated, scalable, and early detection of suicidal ideation and behavior.