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

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 in...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2023-05
Main Authors: Garg, Muskan, Shahbandegan, Amirmohammad, Chadha, Amrit, Mago, Vijay
Format: Article
Language:English
Subjects:
Customization
Datasets
Digital media
Natural language processing
Social networks
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422