WellXplain: Wellness Concept Extraction and Classification in Reddit Posts for Mental Health Analysis
This work addresses the need for automated mental health analysis from social media to support professionals, but it is incremental as it primarily provides a new dataset and benchmark rather than a novel method.
The paper tackles the problem of identifying wellness dimensions in social media content for mental health analysis by introducing WELLXPLAIN, a dataset of 3,092 Reddit posts with 72,813 words and human-annotated wellness concepts based on Dunn's theory, to enable the development of specialized language models for healthcare concept extraction.
During the current mental health crisis, the importance of identifying potential indicators of mental issues from social media content has surged. Overlooking the multifaceted nature of mental and social well-being can have detrimental effects on one's mental state. In traditional therapy sessions, professionals manually pinpoint the origins and outcomes of underlying mental challenges, a process both detailed and time-intensive. We introduce an approach to this intricate mental health analysis by framing the identification of wellness dimensions in Reddit content as a wellness concept extraction and categorization challenge. We've curated a unique dataset named WELLXPLAIN, comprising 3,092 entries and totaling 72,813 words. Drawing from Halbert L. Dunn's well-regarded wellness theory, our team formulated an annotation framework along with guidelines. This dataset also includes human-marked textual segments, offering clear reasoning for decisions made in the wellness concept categorization process. Our aim in publishing this dataset and analyzing initial benchmarks is to spearhead the creation of advanced language models tailored for healthcare-focused concept extraction and categorization.