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MultiWD: Multi-label wellness dimensions in social media posts.

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This study identifies wellness dimensions in online writings using AI. Fine-tuned language models show strong performance in detecting social and mental well-being indicators in user-generated text.

Keywords:
DatasetMental healthMulti-label classificationWellness dimensions

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Area of Science:

  • Computational Linguistics
  • Digital Health
  • Social Computing

Background:

  • Halbert L. Dunn's wellness concept includes social and mental well-being.
  • Neglecting these dimensions can negatively impact mental health.
  • Untreated mental disturbances may escalate to severe disorders.

Purpose of the Study:

  • To develop a fine-grained approach for identifying wellness dimensions in human-written text.
  • To detect indicators of social and mental well-being on social media.
  • To analyze self-narrated writings for wellness dimension presence.

Main Methods:

  • Introduction of the MultiWD dataset (3281 instances) for wellness dimension identification.
  • Utilizing state-of-the-art classifiers for a multi-label classification task.
  • Leveraging fine-tuned large language models and BERT for text analysis.

Main Results:

  • Fine-tuned large language models demonstrated superior performance.
  • The BERT model established a baseline, achieving an F1 score of 76.69.
  • Comparative analysis highlighted the effectiveness of advanced language models.

Conclusions:

  • AI models need trustworthy, domain-specific knowledge for accurate wellness dimension extraction.
  • Developing contextually-aware AI is crucial for comprehensive mental health analysis.
  • The study emphasizes the potential of AI in understanding digital wellness indicators.