@inproceedings{soldaini-etal-2018-helping,
title = "Helping or Hurting? Predicting Changes in Users{'} Risk of Self-Harm Through Online Community Interactions",
author = "Soldaini, Luca and
Walsh, Timothy and
Cohan, Arman and
Han, Julien and
Goharian, Nazli",
editor = "Loveys, Kate and
Niederhoffer, Kate and
Prud{'}hommeaux, Emily and
Resnik, Rebecca and
Resnik, Philip",
booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
month = jun,
year = "2018",
address = "New Orleans, LA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-0621",
doi = "10.18653/v1/W18-0621",
pages = "194--203",
abstract = "In recent years, online communities have formed around suicide and self-harm prevention. While these communities offer support in moment of crisis, they can also normalize harmful behavior, discourage professional treatment, and instigate suicidal ideation. In this work, we focus on how interaction with others in such a community affects the mental state of users who are seeking support. We first build a dataset of conversation threads between users in a distressed state and community members offering support. We then show how to construct a classifier to predict whether distressed users are helped or harmed by the interactions in the thread, and we achieve a macro-F1 score of up to 0.69.",
}
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<abstract>In recent years, online communities have formed around suicide and self-harm prevention. While these communities offer support in moment of crisis, they can also normalize harmful behavior, discourage professional treatment, and instigate suicidal ideation. In this work, we focus on how interaction with others in such a community affects the mental state of users who are seeking support. We first build a dataset of conversation threads between users in a distressed state and community members offering support. We then show how to construct a classifier to predict whether distressed users are helped or harmed by the interactions in the thread, and we achieve a macro-F1 score of up to 0.69.</abstract>
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%0 Conference Proceedings
%T Helping or Hurting? Predicting Changes in Users’ Risk of Self-Harm Through Online Community Interactions
%A Soldaini, Luca
%A Walsh, Timothy
%A Cohan, Arman
%A Han, Julien
%A Goharian, Nazli
%Y Loveys, Kate
%Y Niederhoffer, Kate
%Y Prud’hommeaux, Emily
%Y Resnik, Rebecca
%Y Resnik, Philip
%S Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, LA
%F soldaini-etal-2018-helping
%X In recent years, online communities have formed around suicide and self-harm prevention. While these communities offer support in moment of crisis, they can also normalize harmful behavior, discourage professional treatment, and instigate suicidal ideation. In this work, we focus on how interaction with others in such a community affects the mental state of users who are seeking support. We first build a dataset of conversation threads between users in a distressed state and community members offering support. We then show how to construct a classifier to predict whether distressed users are helped or harmed by the interactions in the thread, and we achieve a macro-F1 score of up to 0.69.
%R 10.18653/v1/W18-0621
%U https://aclanthology.org/W18-0621
%U https://doi.org/10.18653/v1/W18-0621
%P 194-203
Markdown (Informal)
[Helping or Hurting? Predicting Changes in Users’ Risk of Self-Harm Through Online Community Interactions](https://aclanthology.org/W18-0621) (Soldaini et al., CLPsych 2018)
ACL