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