@inproceedings{gollapalli-etal-2021-suicide,
title = "Suicide Risk Prediction by Tracking Self-Harm Aspects in Tweets: {NUS}-{IDS} at the {CLP}sych 2021 Shared Task",
author = "Gollapalli, Sujatha Das and
Zagatti, Guilherme Augusto and
Ng, See-Kiong",
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-1.10",
doi = "10.18653/v1/2021.clpsych-1.10",
pages = "93--98",
abstract = "We describe our system for identifying users at-risk for suicide based on their tweets developed for the CLPsych 2021 Shared Task. Based on research in mental health studies linking self-harm tendencies with suicide, in our system, we attempt to characterize self-harm aspects expressed in user tweets over a period of time. To this end, we design SHTM, a Self-Harm Topic Model that combines Latent Dirichlet Allocation with a self-harm dictionary for modeling daily tweets of users. Next, differences in moods and topics over time are captured as features to train a deep learning model for suicide prediction.",
}
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%0 Conference Proceedings
%T Suicide Risk Prediction by Tracking Self-Harm Aspects in Tweets: NUS-IDS at the CLPsych 2021 Shared Task
%A Gollapalli, Sujatha Das
%A Zagatti, Guilherme Augusto
%A Ng, See-Kiong
%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 gollapalli-etal-2021-suicide
%X We describe our system for identifying users at-risk for suicide based on their tweets developed for the CLPsych 2021 Shared Task. Based on research in mental health studies linking self-harm tendencies with suicide, in our system, we attempt to characterize self-harm aspects expressed in user tweets over a period of time. To this end, we design SHTM, a Self-Harm Topic Model that combines Latent Dirichlet Allocation with a self-harm dictionary for modeling daily tweets of users. Next, differences in moods and topics over time are captured as features to train a deep learning model for suicide prediction.
%R 10.18653/v1/2021.clpsych-1.10
%U https://aclanthology.org/2021.clpsych-1.10
%U https://doi.org/10.18653/v1/2021.clpsych-1.10
%P 93-98
Markdown (Informal)
[Suicide Risk Prediction by Tracking Self-Harm Aspects in Tweets: NUS-IDS at the CLPsych 2021 Shared Task](https://aclanthology.org/2021.clpsych-1.10) (Gollapalli et al., CLPsych 2021)
ACL