electronic media datasets - Mental Health Datasets
An evolving list of electronic media datasets used to model mental health status. This repository curates a variety of datasets from different sources, including social media platforms, online forums, and academic studies, to support research in mental health modeling and AI applications.
معرفی دقیق
The Mental Health Datasets repository is a curated list of datasets that can be used to model and analyze mental health status. It includes datasets from various sources such as Reddit, Twitter, and online support forums, covering a wide range of mental health conditions like depression, anxiety, and suicidal ideation. This resource is invaluable for researchers and developers working on AI models for mental health support and intervention.For an overview of existing datasets, please consider reading the paper 'On the State of Social Media Data for Mental Health Research'.
بیشترمجموعه داده
Chinese Psychological QA DataSet - GitHub Repository
The Chinese Psychological QA DataSet is a collection of 102,845 community Q&A pairs related to psychological topics., providing a rich source of data for research and development in psychological counseling and AI applications. Each entry includes detailed question and answer information, making it a valuable resource for understanding user queries and generating appropriate responses.
Emotional First Aid Dataset: Psychological Counseling QA Corpus
The Emotional First Aid Dataset is a comprehensive Chinese psychological counseling QA corpus, featuring 20,000 multi-turn dialogues. It is designed to support the development of AI applications in the field of psychological counseling and is available for research purposes.
IC-AnnoMI: In-Context MI Dialogues - GitHub Repository
The IC-AnnoMI repository contains source code and a synthetic dataset generated through in-context zero-shot LLM prompting for mental health and therapeutic counselling. IC-AnnoMI is a project that generates contextual MI dialogues using large language models (LLMs). The project contains source code and a synthetic dataset generated through zero-shot prompts, aiming to address the data scarcity and inherent bias problems in mental health and therapeutic consultation.