WellXplain: Wellness Concept Extraction and Classification in Reddit
  Posts for Mental Health Analysis
Muskan Garg
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.