Deep neural networks detect suicide risk from textual Facebook posts

Background: Detection of suicide risk is a highly prioritized, yet complicated task. In fact, five
decades of suicide research produced predictions that were only marginally better than chance
(AUCs = 0.56 – 0.58). Advanced machine learning methods open up new opportunities for
progress in mental health research. In the present study, Artificial Neural Network (ANN)
models were constructed to predict externally valid suicide risk from everyday language of social
media users. Method: The dataset included 83,292 postings authored by 1,002 authenticated,
active Facebook users, alongside clinically valid psychosocial information about the users.
Results: Using Deep Contextualized Word Embeddings (CWEs) for text representation, two
models were constructed: A Single Task Model (STM), to predict suicide risk from Facebook
postings directly (Facebook texts → suicide) and a Multi-Task Model (MTM), which included
hierarchical, multilayered sets of theory-driven risk factors (Facebook texts → personality traits
→ psychosocial risks → psychiatric disorders → suicide). Compared with the STM predictions
(.606 ≤ AUC ≤ .608), the MTM produced improved prediction accuracy (.690 ≤ AUC ≤ .759),
with substantially larger effect sizes (.701 ≤ d ≤ .994). Subsequent content analyses suggest that
predictions did not rely on explicit suicide-related themes, but on a wide range of content.
Conclusions: Advanced machine learning methods can improve our ability to predict suicide
risk from everyday social media activities. The knowledge generated by this research may
eventually lead to the development of more accurate and objective detection tools and get
individuals the help they need in time.