This repository provides code and data for automatic depression detection using a GRU/BiLSTM-based model. It includes an emotional audio-textual corpus designed to support the diagnosis of psychological distress conditions such as anxiety, depression, and post-traumatic stress disorder.
The ICASSP2022-Depression project presents a comprehensive approach to automatic depression detection using deep learning techniques. The repository includes a GRU/BiLSTM-based model and an emotional audio-textual corpus, making it a valuable resource for researchers working on mental health and natural language processing.
The ToM QA Dataset is designed to evaluate question-answering models' ability to reason about beliefs. It includes 3 task types and 4 question types, creating 12 total scenarios. The dataset is inspired by theory-of-mind experiments in developmental psychology and is used to test models' understanding of beliefs and inconsistent states of the world.
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 ISSP is a cross-national collaboration program conducting annual surveys on diverse topics relevant to social sciences. It includes members from various cultures around the globe and provides free access to collected data and documentation.