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During the current mental health crisis, the importance of identifyingpotential indicators of mental issues from social media content has surged.Overlooking the multifaceted nature of mental and social well-being can havedetrimental effects on one's mental state. In traditional therapy sessions,professionals manually pinpoint the origins and outcomes of underlying mentalchallenges, a process both detailed and time-intensive. We introduce anapproach to this intricate mental health analysis by framing the identificationof wellness dimensions in Reddit content as a wellness concept extraction andcategorization 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 annotationframework along with guidelines. This dataset also includes human-markedtextual segments, offering clear reasoning for decisions made in the wellnessconcept categorization process. Our aim in publishing this dataset andanalyzing initial benchmarks is to spearhead the creation of advanced languagemodels tailored for healthcare-focused concept extraction and categorization.

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