@inproceedings{wang-etal-2021-learning,
title = "Learning Models for Suicide Prediction from Social Media Posts",
author = "Wang, Ning and
Fan, Luo and
Shivtare, Yuvraj and
Badal, Varsha and
Subbalakshmi, Koduvayur and
Chandramouli, Rajarathnam and
Lee, Ellen",
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-1.9",
doi = "10.18653/v1/2021.clpsych-1.9",
pages = "87--92",
abstract = "We propose a deep learning architecture and test three other machine learning models to automatically detect individuals that will attempt suicide within (1) 30 days and (2) six months, using their social media post data provided in the CL-Psych-Challenge. Additionally, we create and extract three sets of handcrafted features for suicide detection based on the three-stage theory of suicide and prior work on emotions and the use of pronouns among persons exhibiting suicidal ideations. Extensive experimentations show that some of the traditional machine learning methods outperform the baseline with an F1 score of 0.741 and F2 score of 0.833 on subtask 1 (prediction of a suicide attempt 30 days prior). However, the proposed deep learning method outperforms the baseline with F1 score of 0.737 and F2 score of 0.843 on subtask2 (prediction of suicide 6 months prior).",
}
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<abstract>We propose a deep learning architecture and test three other machine learning models to automatically detect individuals that will attempt suicide within (1) 30 days and (2) six months, using their social media post data provided in the CL-Psych-Challenge. Additionally, we create and extract three sets of handcrafted features for suicide detection based on the three-stage theory of suicide and prior work on emotions and the use of pronouns among persons exhibiting suicidal ideations. Extensive experimentations show that some of the traditional machine learning methods outperform the baseline with an F1 score of 0.741 and F2 score of 0.833 on subtask 1 (prediction of a suicide attempt 30 days prior). However, the proposed deep learning method outperforms the baseline with F1 score of 0.737 and F2 score of 0.843 on subtask2 (prediction of suicide 6 months prior).</abstract>
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%0 Conference Proceedings
%T Learning Models for Suicide Prediction from Social Media Posts
%A Wang, Ning
%A Fan, Luo
%A Shivtare, Yuvraj
%A Badal, Varsha
%A Subbalakshmi, Koduvayur
%A Chandramouli, Rajarathnam
%A Lee, Ellen
%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 wang-etal-2021-learning
%X We propose a deep learning architecture and test three other machine learning models to automatically detect individuals that will attempt suicide within (1) 30 days and (2) six months, using their social media post data provided in the CL-Psych-Challenge. Additionally, we create and extract three sets of handcrafted features for suicide detection based on the three-stage theory of suicide and prior work on emotions and the use of pronouns among persons exhibiting suicidal ideations. Extensive experimentations show that some of the traditional machine learning methods outperform the baseline with an F1 score of 0.741 and F2 score of 0.833 on subtask 1 (prediction of a suicide attempt 30 days prior). However, the proposed deep learning method outperforms the baseline with F1 score of 0.737 and F2 score of 0.843 on subtask2 (prediction of suicide 6 months prior).
%R 10.18653/v1/2021.clpsych-1.9
%U https://aclanthology.org/2021.clpsych-1.9
%U https://doi.org/10.18653/v1/2021.clpsych-1.9
%P 87-92
Markdown (Informal)
[Learning Models for Suicide Prediction from Social Media Posts](https://aclanthology.org/2021.clpsych-1.9) (Wang et al., CLPsych 2021)
ACL
- Ning Wang, Luo Fan, Yuvraj Shivtare, Varsha Badal, Koduvayur Subbalakshmi, Rajarathnam Chandramouli, and Ellen Lee. 2021. Learning Models for Suicide Prediction from Social Media Posts. In Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access, pages 87–92, Online. Association for Computational Linguistics.