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.
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'.
The iBVP dataset is a collection of synchronized RGB and thermal infrared videos with PPG ground-truth signals acquired from an ear. It includes manual signal quality labels and dense signal-quality assessment using the SQA-PhysMD model. The dataset is designed to induce real-world variations in psycho-physiological states and head movement.
This project implements the conversion algorithm from the ToMi dataset to the T4D (Thinking is for Doing) dataset, as introduced in the paper https://arxiv.org/abs/2310.03051. It filters examples with Theory of Mind (ToM) questions and adapts the algorithm to account for second-order false beliefs.
The SimpleToM dataset is designed to evaluate models' ability to reason about beliefs and actions in various scenarios. It includes a variety of situations with multiple choice questions and answers, covering topics such as food items, personal belongings, and service industries.