The Hitchhiker's Guide to Computational Linguistics in Suicide Prevention
Suicide, a leading cause of death, is a complex and a hard-to-predict human tragedy. This article introduces a comprehensive outlook on the emerging movement to integrate Computational Linguistics (CL) in suicide prevention research and practice. Focusing mainly on the state-of-the-art Deep Neural Network models, this "travel guide" article describes, in a relatively plain language, how CL methodologies could facilitate early detection of suicide risk (section 1). Major potential contributions of CL methodologies (e.g., word embeddings, interpretational frameworks) for deepening our theoretical understanding of suicide behaviors (section 2) and promoting the personalized approach in psychological assessment (section 3), are presented as well. Importantly, the article also discusses principal ethical (section 4) and methodological (section 5) obstacles in CL-suicide prevention, such as the difficulty to maintain peoples' privacy/safety or interpret the "black box" of prediction algorithms. Ethical guidelines and practical methodological recommendations addressing these obstacles, are provided for future researchers and clinicians.