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Suicide is a major public health crisis. With more than 20,000,000 suicideattempts each year, the early detection of suicidal intent has the potential tosave hundreds of thousands of lives. Traditional mental health screeningmethods are time-consuming, costly, and often inaccessible to disadvantagedpopulations; online detection of suicidal intent using machine learning offersa viable alternative. Here we present Robin, the largest non-keyword generatedsuicidal corpus to date, consisting of over 1.1 million online forum postings.In addition to its unprecedented size, Robin is specially constructed toinclude various categories of suicidal text, such as suicide bereavement andflippant references, better enabling models trained on Robin to learn thesubtle nuances of text expressing suicidal ideation. Experimental resultsachieve state-of-the-art performance for the classification of suicidal text,both with traditional methods like logistic regression (F1=0.85), as well aswith large-scale pre-trained language models like BERT (F1=0.92). Finally, werelease the Robin dataset publicly as a machine learning resource with thepotential to drive the next generation of suicidal sentiment research.

Robin: A Novel Online Suicidal Text Corpus of Substantial Breadth and Scale