Real-time Topic Models for Crisis Counseling

Karthik Dinakar, J.B.C. Chaney, Henry Lieberman, D. Blei


The proliferation of text-based crisis counseling platforms in recent months has opened an exciting opportunity for applied machine learning to (1) provide practical assistance for human counselors who provide emotional and practical support and (2) analyze counselor-caller interactions to build a landscape of the distribution of mental health issues experienced by callers on an unprecedented scale. We present Fathom, a natural language interface powered by topic models to help crisis counselors on Crisis Text Line, a new 911-like crisis hotline that takes calls via text messaging. We apply a mixed-initiative labeled LDA model to analyze counselor-caller conversations and use them to power real-time visualizations aimed at mitigating counselor cognitive load. We discuss three key aspects of crisis counseling and why topic models are suitable for mining this phenomenon. We propose new variants of topic models inspired by the practical constraints posed by their real-time deployment.

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