@inproceedings{bayram-benhiba-2021-determining,
title = "Determining a Person{'}s Suicide Risk by Voting on the Short-Term History of Tweets for the {CLP}sych 2021 Shared Task",
author = "Bayram, Ulya and
Benhiba, Lamia",
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-1.8",
doi = "10.18653/v1/2021.clpsych-1.8",
pages = "81--86",
abstract = "In this shared task, we accept the challenge of constructing models to identify Twitter users who attempted suicide based on their tweets 30 and 182 days before the adverse event{'}s occurrence. We explore multiple machine learning and deep learning methods to identify a person{'}s suicide risk based on the short-term history of their tweets. Taking the real-life applicability of the model into account, we make the design choice of classifying on the tweet level. By voting the tweet-level suicide risk scores through an ensemble of classifiers, we predict the suicidal users 30-days before the event with an 81.8{\%} true-positives rate. Meanwhile, the tweet-level voting falls short on the six-month-long data as the number of tweets with weak suicidal ideation levels weakens the overall suicidal signals in the long term.",
}
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<abstract>In this shared task, we accept the challenge of constructing models to identify Twitter users who attempted suicide based on their tweets 30 and 182 days before the adverse event’s occurrence. We explore multiple machine learning and deep learning methods to identify a person’s suicide risk based on the short-term history of their tweets. Taking the real-life applicability of the model into account, we make the design choice of classifying on the tweet level. By voting the tweet-level suicide risk scores through an ensemble of classifiers, we predict the suicidal users 30-days before the event with an 81.8% true-positives rate. Meanwhile, the tweet-level voting falls short on the six-month-long data as the number of tweets with weak suicidal ideation levels weakens the overall suicidal signals in the long term.</abstract>
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%0 Conference Proceedings
%T Determining a Person’s Suicide Risk by Voting on the Short-Term History of Tweets for the CLPsych 2021 Shared Task
%A Bayram, Ulya
%A Benhiba, Lamia
%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 bayram-benhiba-2021-determining
%X In this shared task, we accept the challenge of constructing models to identify Twitter users who attempted suicide based on their tweets 30 and 182 days before the adverse event’s occurrence. We explore multiple machine learning and deep learning methods to identify a person’s suicide risk based on the short-term history of their tweets. Taking the real-life applicability of the model into account, we make the design choice of classifying on the tweet level. By voting the tweet-level suicide risk scores through an ensemble of classifiers, we predict the suicidal users 30-days before the event with an 81.8% true-positives rate. Meanwhile, the tweet-level voting falls short on the six-month-long data as the number of tweets with weak suicidal ideation levels weakens the overall suicidal signals in the long term.
%R 10.18653/v1/2021.clpsych-1.8
%U https://aclanthology.org/2021.clpsych-1.8
%U https://doi.org/10.18653/v1/2021.clpsych-1.8
%P 81-86
Markdown (Informal)
[Determining a Person’s Suicide Risk by Voting on the Short-Term History of Tweets for the CLPsych 2021 Shared Task](https://aclanthology.org/2021.clpsych-1.8) (Bayram & Benhiba, CLPsych 2021)
ACL