task_categories:
- fill-mask
language:
- en
tags:
- medical
pretty_name: MentalReddit
size_categories:
- 1M<n<10M
MentalReddit
This dataset, dlb/mentalreddit, was created by the DeepLearningBrasil team for the pre-training of their MentalBERTa model.
This model secured the first position in the DepSign-LT-EDI@RANLP-2023 shared task, which focused on classifying social media texts into three levels of depression.
Dataset Description
The MentalReddit dataset is a large collection of English-language comments sourced from Reddit. The data was specifically curated to provide a rich resource for understanding mental health discourse, as well as general language patterns. The dataset is composed of two main parts:
- Mental Health-Related Subreddits: 3.4 million comments from communities focused on mental health topics.
- General Subreddits: 3.2 million comments from a variety of non-depression-related subreddits to provide a broad base of general language.
In total, the dataset contains approximately 7.31 million comments, occupying about 1.4 GB of disk space.
Data Fields
The dataset consists of the following fields:
body: The text content of the Reddit comment.subreddit: The name of the subreddit from which the comment was sourced.id: A unique identifier for the comment.
Usage
You can load the dataset using the Hugging Face datasets library:
from datasets import load_dataset
dataset = load_dataset("dlb/mentalreddit")
Citation
@inproceedings{garcia-etal-2023-deeplearningbrasil,
title = "{D}eep{L}earning{B}rasil@{LT}-{EDI}-2023: Exploring Deep Learning Techniques for Detecting Depression in Social Media Text",
author = "Garcia, Eduardo and
Gomes, Juliana and
Barbosa Junior, Adalberto and
Borges, Cardeque and
da Silva, N{\'a}dia",
booktitle = "Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.ltedi-1.42",
pages = "272--278",
}
Acknowledgments
This work has been supported by the AI Center of Excellence (Centro de Excelência em Inteligência Artificial – CEIA) of the Institute of Informatics at the Federal University of Goiás (INF-UFG).