  With a surge in identifying suicidal risk and its severity in social media
posts, we argue that a more consequential and explainable research is required
for optimal impact on clinical psychology practice and personalized mental
healthcare. The success of computational intelligence techniques for inferring
mental illness from social media resources, points to natural language
processing as a lens for determining Interpersonal Risk Factors (IRF) in human
writings. Motivated with limited availability of datasets for social NLP
research community, we construct and release a new annotated dataset with
human-labelled explanations and classification of IRF affecting mental
disturbance on social media: (i) Thwarted Belongingness (TBe), and (ii)
Perceived Burdensomeness (PBu). We establish baseline models on our dataset
facilitating future research directions to develop real-time personalized AI
models by detecting patterns of TBe and PBu in emotional spectrum of user's
historical social media profile.
