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With a surge in identifying suicidal risk and its severity in social mediaposts, we argue that a more consequential and explainable research is requiredfor optimal impact on clinical psychology practice and personalized mentalhealthcare. The success of computational intelligence techniques for inferringmental illness from social media resources, points to natural languageprocessing as a lens for determining Interpersonal Risk Factors (IRF) in humanwritings. Motivated with limited availability of datasets for social NLPresearch community, we construct and release a new annotated dataset withhuman-labelled explanations and classification of IRF affecting mentaldisturbance on social media: (i) Thwarted Belongingness (TBe), and (ii)Perceived Burdensomeness (PBu). We establish baseline models on our datasetfacilitating future research directions to develop real-time personalized AImodels by detecting patterns of TBe and PBu in emotional spectrum of user'shistorical social media profile.

An Annotated Dataset for Explainable Interpersonal Risk Factors of Mental Disturbance in Social Media Posts