TY - GEN
T1 - Learning Models for Suicide Prediction from Social Media Posts
AU - Wang, Ning
AU - Luo, Fan
AU - Shivtare, Yuvraj
AU - Badal, Varsha
AU - Subbalakshmi, K. P.
AU - Chandramouli, R.
AU - Lee, Ellen
N1 - Publisher Copyright:
©2021 Association for Computational Linguistics.
PY - 2021
Y1 - 2021
N2 - 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 (Macavaney et al., 2021) via the CLPsych 2021 shared task. Additionally, we create and extract three sets of handcrafted features for suicide risk 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 subtask 2 (prediction of suicide 6 months prior).
AB - 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 (Macavaney et al., 2021) via the CLPsych 2021 shared task. Additionally, we create and extract three sets of handcrafted features for suicide risk 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 subtask 2 (prediction of suicide 6 months prior).
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M3 - Conference contribution
AN - SCOPUS:85123219530
T3 - Computational Linguistics and Clinical Psychology: Improving Access, CLPsych 2021 - Proceedings of the 7th Workshop, in conjunction with NAACL 2021
SP - 87
EP - 92
BT - Computational Linguistics and Clinical Psychology
A2 - Goharian, Nazli
A2 - Resnik, Philip
A2 - Yates, Andrew
A2 - Ireland, Molly
A2 - Niederhoffer, Kate
A2 - Resnik, Rebecca
T2 - 7th Workshop on Computational Linguistics and Clinical Psychology: Improving Access, CLPsych 2021
Y2 - 11 June 2021
ER -