tartuNLP/reddit-anhedonia by huggingface-mirror (hf-mirror)
Focusing on the PRIMATE dataset, our study reveals concerns regarding annotation validity, particularly for the lack of interest or pleasure symptom. Through re-annotation by a mental health professional, we introduce finer labels and textual spans as evidence, identifying a notable number of false positives. Our refined annotations offer a higher-quality test set for anhedonia detection. This study underscores the necessity of addressing annotation quality issues in mental health datasets, advocating for improved methodologies to enhance NLP model reliability in mental health assessments. A mental health professional (MHP) read all the posts in the subset and labelled them for the presence of loss of interest or pleasure (anhedonia). The MHP assigned three labels to each post: a) 'mentioned' if the symptom is talked about in the text, but it is not possible to infer its duration or intensity; b) 'answerable' if there is clear evidence of anhedonia; c) 'writer's symptoms' which shows whether the author of the post discusses themselves or a third person. Additionally, the MHP selected the part of the text that supports the positive label.
This dataset contains survey responses from individuals in the tech industry about their mental health, including questions about treatment, workplace resources, and attitudes towards discussing mental health in the workplace. By analyzing this dataset, we can better understand how prevalent mental health issues are among those who work in the tech sector—and what kinds of resources they rely upon to find help—so that more can be done to create a healthier working environment for all.
The ISSP is a cross-national collaboration program conducting annual surveys on diverse topics relevant to social sciences. Established in 1984, it includes members from various cultures around the globe. Over one million respondents have participated in ISSP surveys, and all collected data and documentation are available free of charge.
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.