@inproceedings{fine-etal-2020-assessing,
title = "Assessing population-level symptoms of anxiety, depression, and suicide risk in real time using {NLP} applied to social media data",
author = "Fine, Alex and
Crutchley, Patrick and
Blase, Jenny and
Carroll, Joshua and
Coppersmith, Glen",
editor = "Bamman, David and
Hovy, Dirk and
Jurgens, David and
O'Connor, Brendan and
Volkova, Svitlana",
booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlpcss-1.6",
doi = "10.18653/v1/2020.nlpcss-1.6",
pages = "50--54",
abstract = "Prevailing methods for assessing population-level mental health require costly collection of large samples of data through instruments such as surveys, and are thus slow to reflect current, rapidly changing social conditions. This constrains how easily population-level mental health data can be integrated into health and policy decision-making. Here, we demonstrate that natural language processing applied to publicly-available social media data can provide real-time estimates of psychological distress in the population (specifically, English-speaking Twitter users in the US). We examine population-level changes in linguistic correlates of mental health symptoms in response to the COVID-19 pandemic and to the killing of George Floyd. As a case study, we focus on social media data from healthcare providers, compared to a control sample. Our results provide a concrete demonstration of how the tools of computational social science can be applied to provide real-time or near-real-time insight into the impact of public events on mental health.",
}
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%0 Conference Proceedings
%T Assessing population-level symptoms of anxiety, depression, and suicide risk in real time using NLP applied to social media data
%A Fine, Alex
%A Crutchley, Patrick
%A Blase, Jenny
%A Carroll, Joshua
%A Coppersmith, Glen
%Y Bamman, David
%Y Hovy, Dirk
%Y Jurgens, David
%Y O’Connor, Brendan
%Y Volkova, Svitlana
%S Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F fine-etal-2020-assessing
%X Prevailing methods for assessing population-level mental health require costly collection of large samples of data through instruments such as surveys, and are thus slow to reflect current, rapidly changing social conditions. This constrains how easily population-level mental health data can be integrated into health and policy decision-making. Here, we demonstrate that natural language processing applied to publicly-available social media data can provide real-time estimates of psychological distress in the population (specifically, English-speaking Twitter users in the US). We examine population-level changes in linguistic correlates of mental health symptoms in response to the COVID-19 pandemic and to the killing of George Floyd. As a case study, we focus on social media data from healthcare providers, compared to a control sample. Our results provide a concrete demonstration of how the tools of computational social science can be applied to provide real-time or near-real-time insight into the impact of public events on mental health.
%R 10.18653/v1/2020.nlpcss-1.6
%U https://aclanthology.org/2020.nlpcss-1.6
%U https://doi.org/10.18653/v1/2020.nlpcss-1.6
%P 50-54
Markdown (Informal)
[Assessing population-level symptoms of anxiety, depression, and suicide risk in real time using NLP applied to social media data](https://aclanthology.org/2020.nlpcss-1.6) (Fine et al., NLP+CSS 2020)
ACL