Text classification for suicide related tweets

Fatima  Chiroma; Han Liu; Mihaela Cocea

doi:10.1109/ICMLC.2018.8527039

Text classification for suicide related tweets

Fatima Chiroma
, Han Liu
, Mihaela Cocea

School of Computing

Cardiff University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

131 Downloads (Pure)

Abstract

Online social networks have become a vital medium for communication. With these platforms, users have the freedom to share their opinions as well as receive information from a diverse group of people. Although this could be beneficial, there are some growing concerns regarding its negative impact on the safety of its users such as the spread of suicidal ideation. Therefore, in this study, we aim to determine the performance of machine classifiers in identifying suicide-related text from Twitter (tweets). The experiment for the study was conducted using four popular machine classifiers: Decision Tree, Naive Bayes, Random Forest and Support Vector Machine. The results of the experiment showed an F-measure ranging from 0.346 to 0.778 for suicide-related communication, with the best performance being achieved using the Decision Tree classifier.

Original language	English
Title of host publication	Proceedings of the 2018 International Conference on Machine Learning and Cybernetics (ICMLC)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	587-592
Number of pages	6
Volume	2
ISBN (Electronic)	978-1-5386-5214-5
ISBN (Print)	978-1-5386-5215-2
DOIs	https://doi.org/10.1109/ICMLC.2018.8527039
Publication status	Published - 12 Nov 2018
Event	2018 International Conference on Machine Learning and Cybernetics - http://www.icmlc.com/icmlc/welcome.html, Chengdu, China Duration: 15 Jul 2018 → 18 Jul 2018

Publication series

Name	International Conference on Machine Learning and Cybernetics (ICMLC)
Publisher	IEEE
ISSN (Print)	2160-133X
ISSN (Electronic)	2160-1348

Conference

Conference	2018 International Conference on Machine Learning and Cybernetics
Abbreviated title	ICMLC 2018
Country/Territory	China
City	Chengdu
Period	15/07/18 → 18/07/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Text classification
Machine Classifier
Social media
Suicide

Access to Document

10.1109/ICMLC.2018.8527039

ICMLC12018
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript (Post-print), 130 KB

Cite this

Chiroma, F., Liu, H., & Cocea, M. (2018). Text classification for suicide related tweets. In Proceedings of the 2018 International Conference on Machine Learning and Cybernetics (ICMLC) (Vol. 2, pp. 587-592). (International Conference on Machine Learning and Cybernetics (ICMLC)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLC.2018.8527039

@inproceedings{f19c5387aa814428b8566cf09cd2b330,

title = "Text classification for suicide related tweets",

abstract = "Online social networks have become a vital medium for communication. With these platforms, users have the freedom to share their opinions as well as receive information from a diverse group of people. Although this could be beneficial, there are some growing concerns regarding its negative impact on the safety of its users such as the spread of suicidal ideation. Therefore, in this study, we aim to determine the performance of machine classifiers in identifying suicide-related text from Twitter (tweets). The experiment for the study was conducted using four popular machine classifiers: Decision Tree, Naive Bayes, Random Forest and Support Vector Machine. The results of the experiment showed an F-measure ranging from 0.346 to 0.778 for suicide-related communication, with the best performance being achieved using the Decision Tree classifier.",

keywords = "Text classification, Machine Classifier, Social media, Suicide",

author = "Fatima Chiroma and Han Liu and Mihaela Cocea",

year = "2018",

month = nov,

day = "12",

doi = "10.1109/ICMLC.2018.8527039",

language = "English",

isbn = "978-1-5386-5215-2",

volume = "2",

series = "International Conference on Machine Learning and Cybernetics (ICMLC)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "587--592",

booktitle = "Proceedings of the 2018 International Conference on Machine Learning and Cybernetics (ICMLC)",

address = "United States",

note = "2018 International Conference on Machine Learning and Cybernetics, ICMLC 2018 ; Conference date: 15-07-2018 Through 18-07-2018",

}

Chiroma, F, Liu, H & Cocea, M 2018, Text classification for suicide related tweets. in Proceedings of the 2018 International Conference on Machine Learning and Cybernetics (ICMLC). vol. 2, International Conference on Machine Learning and Cybernetics (ICMLC), Institute of Electrical and Electronics Engineers Inc., pp. 587-592, 2018 International Conference on Machine Learning and Cybernetics, Chengdu, China, 15/07/18. https://doi.org/10.1109/ICMLC.2018.8527039

Text classification for suicide related tweets. / Chiroma, Fatima ; Liu, Han; Cocea, Mihaela.
Proceedings of the 2018 International Conference on Machine Learning and Cybernetics (ICMLC). Vol. 2 Institute of Electrical and Electronics Engineers Inc., 2018. p. 587-592 (International Conference on Machine Learning and Cybernetics (ICMLC)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Text classification for suicide related tweets

AU - Chiroma, Fatima

AU - Liu, Han

AU - Cocea, Mihaela

PY - 2018/11/12

Y1 - 2018/11/12

N2 - Online social networks have become a vital medium for communication. With these platforms, users have the freedom to share their opinions as well as receive information from a diverse group of people. Although this could be beneficial, there are some growing concerns regarding its negative impact on the safety of its users such as the spread of suicidal ideation. Therefore, in this study, we aim to determine the performance of machine classifiers in identifying suicide-related text from Twitter (tweets). The experiment for the study was conducted using four popular machine classifiers: Decision Tree, Naive Bayes, Random Forest and Support Vector Machine. The results of the experiment showed an F-measure ranging from 0.346 to 0.778 for suicide-related communication, with the best performance being achieved using the Decision Tree classifier.

AB - Online social networks have become a vital medium for communication. With these platforms, users have the freedom to share their opinions as well as receive information from a diverse group of people. Although this could be beneficial, there are some growing concerns regarding its negative impact on the safety of its users such as the spread of suicidal ideation. Therefore, in this study, we aim to determine the performance of machine classifiers in identifying suicide-related text from Twitter (tweets). The experiment for the study was conducted using four popular machine classifiers: Decision Tree, Naive Bayes, Random Forest and Support Vector Machine. The results of the experiment showed an F-measure ranging from 0.346 to 0.778 for suicide-related communication, with the best performance being achieved using the Decision Tree classifier.

KW - Text classification

KW - Machine Classifier

KW - Social media

KW - Suicide

UR - https://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000424

UR - http://www.icmlc.com/

U2 - 10.1109/ICMLC.2018.8527039

DO - 10.1109/ICMLC.2018.8527039

M3 - Conference contribution

SN - 978-1-5386-5215-2

VL - 2

T3 - International Conference on Machine Learning and Cybernetics (ICMLC)

SP - 587

EP - 592

BT - Proceedings of the 2018 International Conference on Machine Learning and Cybernetics (ICMLC)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 International Conference on Machine Learning and Cybernetics

Y2 - 15 July 2018 through 18 July 2018

ER -

Text classification for suicide related tweets

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Fingerprint

Cite this