@inproceedings{jiang-etal-2021-automatic,
title = "Automatic Detection and Prediction of Psychiatric Hospitalizations From Social Media Posts",
author = "Jiang, Zhengping and
Zomick, Jonathan and
Levitan, Sarah Ita and
Serper, Mark and
Hirschberg, Julia",
editor = "Goharian, Nazli and
Resnik, Philip and
Yates, Andrew and
Ireland, Molly and
Niederhoffer, Kate and
Resnik, Rebecca",
booktitle = "Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.clpsych-1.14",
doi = "10.18653/v1/2021.clpsych-1.14",
pages = "116--121",
abstract = "We address the problem of predicting psychiatric hospitalizations using linguistic features drawn from social media posts. We formulate this novel task and develop an approach to automatically extract time spans of self-reported psychiatric hospitalizations. Using this dataset, we build predictive models of psychiatric hospitalization, comparing feature sets, user vs. post classification, and comparing model performance using a varying time window of posts. Our best model achieves an F1 of .718 using 7 days of posts. Our results suggest that this is a useful framework for collecting hospitalization data, and that social media data can be leveraged to predict acute psychiatric crises before they occur, potentially saving lives and improving outcomes for individuals with mental illness.",
}
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%0 Conference Proceedings
%T Automatic Detection and Prediction of Psychiatric Hospitalizations From Social Media Posts
%A Jiang, Zhengping
%A Zomick, Jonathan
%A Levitan, Sarah Ita
%A Serper, Mark
%A Hirschberg, Julia
%Y Goharian, Nazli
%Y Resnik, Philip
%Y Yates, Andrew
%Y Ireland, Molly
%Y Niederhoffer, Kate
%Y Resnik, Rebecca
%S Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F jiang-etal-2021-automatic
%X We address the problem of predicting psychiatric hospitalizations using linguistic features drawn from social media posts. We formulate this novel task and develop an approach to automatically extract time spans of self-reported psychiatric hospitalizations. Using this dataset, we build predictive models of psychiatric hospitalization, comparing feature sets, user vs. post classification, and comparing model performance using a varying time window of posts. Our best model achieves an F1 of .718 using 7 days of posts. Our results suggest that this is a useful framework for collecting hospitalization data, and that social media data can be leveraged to predict acute psychiatric crises before they occur, potentially saving lives and improving outcomes for individuals with mental illness.
%R 10.18653/v1/2021.clpsych-1.14
%U https://aclanthology.org/2021.clpsych-1.14
%U https://doi.org/10.18653/v1/2021.clpsych-1.14
%P 116-121
Markdown (Informal)
[Automatic Detection and Prediction of Psychiatric Hospitalizations From Social Media Posts](https://aclanthology.org/2021.clpsych-1.14) (Jiang et al., CLPsych 2021)
ACL