Under the hood: Suicide prevention tools powered by AI

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This post details the technical evolution of Meta's AI-powered suicide prevention tools. It describes the transition from relying on user reports to using machine learning for proactive detection. Key technical details include: the use of FBLearner for training classifiers, the application of n-gram-based linear regression for initial text classification, the incorporation of DeepText neural networks for more nuanced text understanding, and the use of a random forest algorithm to combine various signals (post text, comments, time of day, day of week, post type, reaction counts) into a final risk score. It also highlights the development of multilingual classifiers to extend coverage to more languages.