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.

当前精神健康危机中，从社交媒体内容中识别潜在的心理问题指标的重要性迅速增加。我们通过将 Reddit 内容中的健康维度的识别视为对健康概念提取和分类的挑战，引入了一种复杂的心理健康分析方法。我们构建了一个名为 WELLXPLAIN 的独特数据集，包括 3,092 个条目和总计 72,813 个词。该数据集还包括人工标注的文本片段，对健康概念分类过程中的决策提供了清晰的理由。我们发布这一数据集并分析初始基准的目的是引领面向医疗保健领域概念提取和分类的先进语言模型的创建。