@inproceedings{bitew-etal-2019-predicting,
title = "Predicting Suicide Risk from Online Postings in {R}eddit The {UG}ent-{IDL}ab submission to the {CLP}ysch 2019 Shared Task A",
author = "Bitew, Semere Kiros and
Bekoulis, Giannis and
Deleu, Johannes and
Sterckx, Lucas and
Zaporojets, Klim and
Demeester, Thomas and
Develder, Chris",
editor = "Niederhoffer, Kate and
Hollingshead, Kristy and
Resnik, Philip and
Resnik, Rebecca and
Loveys, Kate",
booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-3019",
doi = "10.18653/v1/W19-3019",
pages = "158--161",
abstract = "This paper describes IDLab{'}s text classification systems submitted to Task A as part of the CLPsych 2019 shared task. The aim of this shared task was to develop automated systems that predict the degree of suicide risk of people based on their posts on Reddit. Bag-of-words features, emotion features and post level predictions are used to derive user-level predictions. Linear models and ensembles of these models are used to predict final scores. We find that predicting fine-grained risk levels is much more difficult than flagging potentially at-risk users. Furthermore, we do not find clear added value from building richer ensembles compared to simple baselines, given the available training data and the nature of the prediction task.",
}
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<abstract>This paper describes IDLab’s text classification systems submitted to Task A as part of the CLPsych 2019 shared task. The aim of this shared task was to develop automated systems that predict the degree of suicide risk of people based on their posts on Reddit. Bag-of-words features, emotion features and post level predictions are used to derive user-level predictions. Linear models and ensembles of these models are used to predict final scores. We find that predicting fine-grained risk levels is much more difficult than flagging potentially at-risk users. Furthermore, we do not find clear added value from building richer ensembles compared to simple baselines, given the available training data and the nature of the prediction task.</abstract>
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%0 Conference Proceedings
%T Predicting Suicide Risk from Online Postings in Reddit The UGent-IDLab submission to the CLPysch 2019 Shared Task A
%A Bitew, Semere Kiros
%A Bekoulis, Giannis
%A Deleu, Johannes
%A Sterckx, Lucas
%A Zaporojets, Klim
%A Demeester, Thomas
%A Develder, Chris
%Y Niederhoffer, Kate
%Y Hollingshead, Kristy
%Y Resnik, Philip
%Y Resnik, Rebecca
%Y Loveys, Kate
%S Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F bitew-etal-2019-predicting
%X This paper describes IDLab’s text classification systems submitted to Task A as part of the CLPsych 2019 shared task. The aim of this shared task was to develop automated systems that predict the degree of suicide risk of people based on their posts on Reddit. Bag-of-words features, emotion features and post level predictions are used to derive user-level predictions. Linear models and ensembles of these models are used to predict final scores. We find that predicting fine-grained risk levels is much more difficult than flagging potentially at-risk users. Furthermore, we do not find clear added value from building richer ensembles compared to simple baselines, given the available training data and the nature of the prediction task.
%R 10.18653/v1/W19-3019
%U https://aclanthology.org/W19-3019
%U https://doi.org/10.18653/v1/W19-3019
%P 158-161
Markdown (Informal)
[Predicting Suicide Risk from Online Postings in Reddit The UGent-IDLab submission to the CLPysch 2019 Shared Task A](https://aclanthology.org/W19-3019) (Bitew et al., CLPsych 2019)
ACL