Towards ordinal suicide ideation detection on social media
Sawhney, R., Joshi, H., Gandhi, S., & Shah, R.R.
The rising ubiquity of social media presents a platform for individuals to express suicide ideation, instead of traditional, formal clinical settings. While neural methods for assessing suicide risk on social media have shown promise, a crippling limitation of existing solutions is that they ignore the inherent ordinal nature across finegrain levels of suicide risk. To this end, we reformulate suicide risk assessment as an Ordinal Regression problem, over the ColumbiaSuicide Severity Scale. We propose SISMO, a hierarchical attention model optimized to factor in the graded nature of increasing suicide risk levels, through soft probability distribution since not all wrong risk-levels are equally wrong. We establish the face value of SISMO for preliminary suicide risk assessment on real-world Reddit data annotated by clinical experts. We conclude by discussing the empirical, practical, and ethical considerations pertaining to SISMO in a larger picture, as a human-in-the-loop framework.