@inproceedings{jiang-etal-2020-detection,
title = "Detection of Mental Health from {R}eddit via Deep Contextualized Representations",
author = "Jiang, Zhengping and
Levitan, Sarah Ita and
Zomick, Jonathan and
Hirschberg, Julia",
editor = "Holderness, Eben and
Jimeno Yepes, Antonio and
Lavelli, Alberto and
Minard, Anne-Lyse and
Pustejovsky, James and
Rinaldi, Fabio",
booktitle = "Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.louhi-1.16",
doi = "10.18653/v1/2020.louhi-1.16",
pages = "147--156",
abstract = "We address the problem of automatic detection of psychiatric disorders from the linguistic content of social media posts. We build a large scale dataset of Reddit posts from users with eight disorders and a control user group. We extract and analyze linguistic characteristics of posts and identify differences between diagnostic groups. We build strong classification models based on deep contextualized word representations and show that they outperform previously applied statistical models with simple linguistic features by large margins. We compare user-level and post-level classification performance, as well as an ensembled multiclass model.",
}
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<abstract>We address the problem of automatic detection of psychiatric disorders from the linguistic content of social media posts. We build a large scale dataset of Reddit posts from users with eight disorders and a control user group. We extract and analyze linguistic characteristics of posts and identify differences between diagnostic groups. We build strong classification models based on deep contextualized word representations and show that they outperform previously applied statistical models with simple linguistic features by large margins. We compare user-level and post-level classification performance, as well as an ensembled multiclass model.</abstract>
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%0 Conference Proceedings
%T Detection of Mental Health from Reddit via Deep Contextualized Representations
%A Jiang, Zhengping
%A Levitan, Sarah Ita
%A Zomick, Jonathan
%A Hirschberg, Julia
%Y Holderness, Eben
%Y Jimeno Yepes, Antonio
%Y Lavelli, Alberto
%Y Minard, Anne-Lyse
%Y Pustejovsky, James
%Y Rinaldi, Fabio
%S Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F jiang-etal-2020-detection
%X We address the problem of automatic detection of psychiatric disorders from the linguistic content of social media posts. We build a large scale dataset of Reddit posts from users with eight disorders and a control user group. We extract and analyze linguistic characteristics of posts and identify differences between diagnostic groups. We build strong classification models based on deep contextualized word representations and show that they outperform previously applied statistical models with simple linguistic features by large margins. We compare user-level and post-level classification performance, as well as an ensembled multiclass model.
%R 10.18653/v1/2020.louhi-1.16
%U https://aclanthology.org/2020.louhi-1.16
%U https://doi.org/10.18653/v1/2020.louhi-1.16
%P 147-156
Markdown (Informal)
[Detection of Mental Health from Reddit via Deep Contextualized Representations](https://aclanthology.org/2020.louhi-1.16) (Jiang et al., Louhi 2020)
ACL