In an era where online platforms increasingly host critical conversations on mental health, understanding the evolution of these discussions is vital. Traditional analysis methods, while insightful, often overlook the sequential interplay of emotions and themes, a gap our research aims to fill. This research paper introduces the Temporal Emotional and Thematic Progression (TETP) methodology, a novel approach that tracks the temporal dynamics of mental health discourse on social media by charting discussions within a two-dimensional emotional-thematic space. Utilizing sentiment analysis and Latent Dirichlet Allocation (LDA) for thematic extraction, we convert discussion sequences into trajectories, revealing patterns in how users navigate through emotional and thematic phases over time. A preliminary analysis of mental health-related posts on Reddit not only confirms the viability of TETP but also uncovers distinct pathways of discourse evolution, offering insights into the collective journey of online communities grappling with mental health issues. By elucidating the temporal aspect of online discussions, TETP aims to enhance understanding of digital mental health landscapes, improve platform moderation strategies, and inform mental health practitioners about prevalent online discourse patterns, ultimately contributing to better online support ecosystems for mental health.

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Temporal Emotional and Thematic Progression (TETP): A Novel Analysis of Mental Health Discussions on Social Platforms

  • Sharath Kumar Jagannathan,
  • Gulhan Bizel

摘要

In an era where online platforms increasingly host critical conversations on mental health, understanding the evolution of these discussions is vital. Traditional analysis methods, while insightful, often overlook the sequential interplay of emotions and themes, a gap our research aims to fill. This research paper introduces the Temporal Emotional and Thematic Progression (TETP) methodology, a novel approach that tracks the temporal dynamics of mental health discourse on social media by charting discussions within a two-dimensional emotional-thematic space. Utilizing sentiment analysis and Latent Dirichlet Allocation (LDA) for thematic extraction, we convert discussion sequences into trajectories, revealing patterns in how users navigate through emotional and thematic phases over time. A preliminary analysis of mental health-related posts on Reddit not only confirms the viability of TETP but also uncovers distinct pathways of discourse evolution, offering insights into the collective journey of online communities grappling with mental health issues. By elucidating the temporal aspect of online discussions, TETP aims to enhance understanding of digital mental health landscapes, improve platform moderation strategies, and inform mental health practitioners about prevalent online discourse patterns, ultimately contributing to better online support ecosystems for mental health.