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Comparing automatically extracted topics from online suicidal ideation and the responses they invoke

Authors:

Alexander Morain,

Jonathan Gemmell,

Daniela RaicuAuthors Info & Claims

SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

Pages 1818 - 1825

https://doi.org/10.1145/3341105.3373902

Published: 30 March 2020 Publication History

Abstract

Suicide is a national public health concern, claiming over one million lives each year worldwide. The ability to understand, identify, and respond to suicidal behavior remains a key priority in preventing suicide. As online social networks have grown in accessibility and popularity, it is increasingly common for users to both discuss mental health and receive support from others online. These online conversations have previously been evaluated by analyzing the language features of social media posts and detecting risk factors and levels of distress among users. In this work, we use natural language processing tools to automatically extract informal topics within posts discussing suicidal ideation and the responses to these posts. Our evaluation demonstrates that frequent topics within the posts represent psychiatrically defined risk factors for suicide, and frequent topics within the responses represent CDC recommended responses to suicidal ideation based on identified protective factors.

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Cited By

Mieles Toloza IDelgado Meza J(2024)Análisis del Lenguaje Natural para la Identificación de Alteraciones Mentales en Redes Sociales: Una Revisión Sistemática de EstudiosRevista Politécnica10.33333/rp.vol53n1.0653:1(57-72)Online publication date: 9-Feb-2024
https://doi.org/10.33333/rp.vol53n1.06
Chen XGenc Y(2022)A Systematic Review of Artificial Intelligence and Mental Health in the Context of Social MediaArtificial Intelligence in HCI10.1007/978-3-031-05643-7_23(353-368)Online publication date: 15-May-2022
https://doi.org/10.1007/978-3-031-05643-7_23
Wang RYang BMa YWang PYu QZong XHuang ZMa SHu LHwang KLiu Z(2021)Medical-Level Suicide Risk Analysis: A Novel Standard and Evaluation ModelIEEE Internet of Things Journal10.1109/JIOT.2021.30523638:23(16825-16834)Online publication date: 1-Dec-2021
https://doi.org/10.1109/JIOT.2021.3052363

Index Terms

Comparing automatically extracted topics from online suicidal ideation and the responses they invoke

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cover image ACM Conferences

SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

March 2020

2348 pages

ISBN:9781450368667

DOI:10.1145/3341105

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University
,
Tomas Cerny
Baylor University
,
Program Chairs:
Dongwan Shin
New Mexico Tech
,
Alessio Bechini
University of Pisa, Italy

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

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Published: 30 March 2020

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National Science Foundation

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SAC '20

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SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing

March 30 - April 3, 2020

Brno, Czech Republic

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Mieles Toloza IDelgado Meza J(2024)Análisis del Lenguaje Natural para la Identificación de Alteraciones Mentales en Redes Sociales: Una Revisión Sistemática de EstudiosRevista Politécnica10.33333/rp.vol53n1.0653:1(57-72)Online publication date: 9-Feb-2024
https://doi.org/10.33333/rp.vol53n1.06
Chen XGenc Y(2022)A Systematic Review of Artificial Intelligence and Mental Health in the Context of Social MediaArtificial Intelligence in HCI10.1007/978-3-031-05643-7_23(353-368)Online publication date: 15-May-2022
https://doi.org/10.1007/978-3-031-05643-7_23
Wang RYang BMa YWang PYu QZong XHuang ZMa SHu LHwang KLiu Z(2021)Medical-Level Suicide Risk Analysis: A Novel Standard and Evaluation ModelIEEE Internet of Things Journal10.1109/JIOT.2021.30523638:23(16825-16834)Online publication date: 1-Dec-2021
https://doi.org/10.1109/JIOT.2021.3052363

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