@inproceedings{10b1ecf8bbce447bb5b289e4cf8bf6bc,
title = "Learning Models for Suicide Prediction from Social Media Posts",
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 (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).",
author = "Ning Wang and Fan Luo and Yuvraj Shivtare and Varsha Badal and Subbalakshmi, {K. P.} and R. Chandramouli and Ellen Lee",
note = "Publisher Copyright: {\textcopyright}2021 Association for Computational Linguistics.; 7th Workshop on Computational Linguistics and Clinical Psychology: Improving Access, CLPsych 2021 ; Conference date: 11-06-2021",
year = "2021",
language = "English",
series = "Computational Linguistics and Clinical Psychology: Improving Access, CLPsych 2021 - Proceedings of the 7th Workshop, in conjunction with NAACL 2021",
publisher = "Association for Computational Linguistics (ACL)",
pages = "87--92",
editor = "Nazli Goharian and Philip Resnik and Andrew Yates and Molly Ireland and Kate Niederhoffer and Rebecca Resnik",
booktitle = "Computational Linguistics and Clinical Psychology",
}
