@inproceedings{cohan-etal-2018-smhd,
title = "{SMHD}: a Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions",
author = "Cohan, Arman and
Desmet, Bart and
Yates, Andrew and
Soldaini, Luca and
MacAvaney, Sean and
Goharian, Nazli",
editor = "Bender, Emily M. and
Derczynski, Leon and
Isabelle, Pierre",
booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/C18-1126",
pages = "1485--1497",
abstract = "Mental health is a significant and growing public health concern. As language usage can be leveraged to obtain crucial insights into mental health conditions, there is a need for large-scale, labeled, mental health-related datasets of users who have been diagnosed with one or more of such conditions. In this paper, we investigate the creation of high-precision patterns to identify self-reported diagnoses of nine different mental health conditions, and obtain high-quality labeled data without the need for manual labelling. We introduce the SMHD (Self-reported Mental Health Diagnoses) dataset and make it available. SMHD is a novel large dataset of social media posts from users with one or multiple mental health conditions along with matched control users. We examine distinctions in users{'} language, as measured by linguistic and psychological variables. We further explore text classification methods to identify individuals with mental conditions through their language.",
}
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%0 Conference Proceedings
%T SMHD: a Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions
%A Cohan, Arman
%A Desmet, Bart
%A Yates, Andrew
%A Soldaini, Luca
%A MacAvaney, Sean
%A Goharian, Nazli
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F cohan-etal-2018-smhd
%X Mental health is a significant and growing public health concern. As language usage can be leveraged to obtain crucial insights into mental health conditions, there is a need for large-scale, labeled, mental health-related datasets of users who have been diagnosed with one or more of such conditions. In this paper, we investigate the creation of high-precision patterns to identify self-reported diagnoses of nine different mental health conditions, and obtain high-quality labeled data without the need for manual labelling. We introduce the SMHD (Self-reported Mental Health Diagnoses) dataset and make it available. SMHD is a novel large dataset of social media posts from users with one or multiple mental health conditions along with matched control users. We examine distinctions in users’ language, as measured by linguistic and psychological variables. We further explore text classification methods to identify individuals with mental conditions through their language.
%U https://aclanthology.org/C18-1126
%P 1485-1497
Markdown (Informal)
[SMHD: a Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions](https://aclanthology.org/C18-1126) (Cohan et al., COLING 2018)
ACL