The field of mental health support and social media analysis is rapidly evolving, with a growing focus on developing AI-driven tools to improve user well-being. Recent research has highlighted the importance of interpretable and context-aware models for generating effective emotional support dialogues. Additionally, there is a increasing interest in understanding social support needs in online communities and developing frameworks to identify and address these needs. Noteworthy papers in this area include Mind2, which proposes a cognitive discourse analysis approach to generate effective emotional support dialogues, and HA-SOS, which develops a hybrid framework to identify social support needs in online questions. Another significant direction is the analysis of social media data to enhance traditional methods of depression screening and the development of datasets such as RedditESS to support advanced AI-driven mental health interventions.
Advances in AI-Driven Mental Health Support and Social Media Analysis
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Mind2: Mind-to-Mind Emotional Support System with Bidirectional Cognitive Discourse Analysis
Understanding Social Support Needs in Questions: A Hybrid Approach Integrating Semi-Supervised Learning and LLM-based Data Augmentation
Reimagining Support: Exploring Autistic Individuals' Visions for AI in Coping with Negative Self-Talk
Kintsugi-Inspired Design: Communicatively Reconstructing Identities Online After Trauma
REALM: A Dataset of Real-World LLM Use Cases
From the CDC to emerging infection disease publics: The long-now of polarizing and complex health crises
Improving User Behavior Prediction: Leveraging Annotator Metadata in Supervised Machine Learning Models
Toward a Healthier Social Media Experience: Designing 'Inspiration' and 'Reality' Modes to Enhance Digital Well-Being for Generation Z
Datasets for Depression Modeling in Social Media: An Overview
RedditESS: A Mental Health Social Support Interaction Dataset -- Understanding Effective Social Support to Refine AI-Driven Support Tools
Socially Constructed Treatment Plans: Analyzing Online Peer Interactions to Understand How Patients Navigate Complex Medical Conditions