Detection of suicidal Twitter posts

Fatima Modibbo Chiroma; Mihaela Cocea; Han Liu

doi:10.1007/978-3-030-29933-0_26

Detection of suicidal Twitter posts

Fatima Modibbo Chiroma, Mihaela Cocea, Han Liu

School of Computing

Cardiff University

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

313 Downloads (Pure)

Abstract

As web data evolves, new technological challenges arise and one of the contributing factors to these challenges is the online social networks. Although they have some benets, their negative impact on vulnerable users such as the spread of suicidal ideation is concerning. As such, it is vital to ne tune the approaches and techniques in order to understand the users and their context for early intervention. Therefore, in this study, we measured the impact of data manipulation and feature extraction, specifically using N-grams, on suicide-related social network text (tweets). We propose a diversified ensemble approach (multi-classifier fusion) to improve the detection of suicide-related text classification. Four machine classifiers were used for the fusion: Support Vector Machine, Random Forest, Naive Bayes and Decision Tree. The results of our proposed approach have shown that the multi-classifier fusion has improved the detection of suicide-related text and, also, that Support Vector Machine has shown some promising results when dealing
with multi-class datasets.

Original language	English
Title of host publication	Advances in Computational Intelligence Systems
Subtitle of host publication	UKCI 2019
Editors	Zhojie Ju, Longzhi Yang, Chenguang Yang, Alexander Gegov, Dalin Zhou
Publisher	Springer
Pages	307-318
Number of pages	12
ISBN (Electronic)	978-3-030-29933-0
ISBN (Print)	978-3-030-29932-3
DOIs	https://doi.org/10.1007/978-3-030-29933-0_26
Publication status	Published - 30 Aug 2019
Event	19th UK Workshop on Computational Intelligence - Portsmouth, United Kingdom Duration: 4 Sept 2019 → 5 Sept 2019 Conference number: 19 https://www.ukci2019.port.ac.uk/

Publication series

Name	Advances in Intelligent Systems and Computing
Publisher	Springer
Volume	1043
ISSN (Print)	2194-5357

Workshop

Workshop	19th UK Workshop on Computational Intelligence
Abbreviated title	UKCI 2019
Country/Territory	United Kingdom
City	Portsmouth
Period	4/09/19 → 5/09/19
Other	The UKCI 2019 covers both theory and applications in computational intelligence. The topics of interest include Fuzzy Systems Neural Networks Evolutionary Computation Evolving Systems Machine Learning Data Mining Cognitive Computing Intelligent Robotics Hybrid Methods Deep Learning Applications of Computational Intelligence
Internet address	https://www.ukci2019.port.ac.uk/

Keywords

Ensemble Learning
Suicide-related Tweets
Text Classification

UN SDGs

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

Access to Document

10.1007/978-3-030-29933-0_26

UKCI2019
This is a post-peer-review, pre-copyedit version of an article published in Ju Z., Yang L., Yang C., Gegov A., Zhou D. (eds) Advances in Computational Intelligence Systems. UKCI 2019. Advances in Intelligent Systems and Computing, vol 1043. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-29933-0_26.
Accepted author manuscript (Post-print), 395 KB

https://link.springer.com/chapter/10.1007/978-3-030-29933-0_26

Cite this

@inproceedings{7b393b6ada9045b5aa68541027819736,

title = "Detection of suicidal Twitter posts",

abstract = "As web data evolves, new technological challenges arise and one of the contributing factors to these challenges is the online social networks. Although they have some benets, their negative impact on vulnerable users such as the spread of suicidal ideation is concerning. As such, it is vital to ne tune the approaches and techniques in order to understand the users and their context for early intervention. Therefore, in this study, we measured the impact of data manipulation and feature extraction, specifically using N-grams, on suicide-related social network text (tweets). We propose a diversified ensemble approach (multi-classifier fusion) to improve the detection of suicide-related text classification. Four machine classifiers were used for the fusion: Support Vector Machine, Random Forest, Naive Bayes and Decision Tree. The results of our proposed approach have shown that the multi-classifier fusion has improved the detection of suicide-related text and, also, that Support Vector Machine has shown some promising results when dealingwith multi-class datasets.",

keywords = "Ensemble Learning, Suicide-related Tweets, Text Classification",

author = "Chiroma, {Fatima Modibbo} and Mihaela Cocea and Han Liu",

year = "2019",

month = aug,

day = "30",

doi = "10.1007/978-3-030-29933-0_26",

language = "English",

isbn = "978-3-030-29932-3",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer",

pages = "307--318",

editor = "Zhojie Ju and Yang, {Longzhi } and Chenguang Yang and Alexander Gegov and Dalin Zhou",

booktitle = "Advances in Computational Intelligence Systems",

note = "19th UK Workshop on Computational Intelligence, UKCI 2019 ; Conference date: 04-09-2019 Through 05-09-2019",

url = "https://www.ukci2019.port.ac.uk/",

}

Chiroma, FM, Cocea, M & Liu, H 2019, Detection of suicidal Twitter posts. in Z Ju, L Yang, C Yang, A Gegov & D Zhou (eds), Advances in Computational Intelligence Systems: UKCI 2019. Advances in Intelligent Systems and Computing, vol. 1043, Springer, pp. 307-318, 19th UK Workshop on Computational Intelligence, Portsmouth, United Kingdom, 4/09/19. https://doi.org/10.1007/978-3-030-29933-0_26

Detection of suicidal Twitter posts. / Chiroma, Fatima Modibbo; Cocea, Mihaela; Liu, Han.
Advances in Computational Intelligence Systems: UKCI 2019. ed. / Zhojie Ju; Longzhi Yang; Chenguang Yang; Alexander Gegov; Dalin Zhou. Springer, 2019. p. 307-318 (Advances in Intelligent Systems and Computing; Vol. 1043).

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

TY - GEN

T1 - Detection of suicidal Twitter posts

AU - Chiroma, Fatima Modibbo

AU - Cocea, Mihaela

AU - Liu, Han

N1 - Conference code: 19

PY - 2019/8/30

Y1 - 2019/8/30

N2 - As web data evolves, new technological challenges arise and one of the contributing factors to these challenges is the online social networks. Although they have some benets, their negative impact on vulnerable users such as the spread of suicidal ideation is concerning. As such, it is vital to ne tune the approaches and techniques in order to understand the users and their context for early intervention. Therefore, in this study, we measured the impact of data manipulation and feature extraction, specifically using N-grams, on suicide-related social network text (tweets). We propose a diversified ensemble approach (multi-classifier fusion) to improve the detection of suicide-related text classification. Four machine classifiers were used for the fusion: Support Vector Machine, Random Forest, Naive Bayes and Decision Tree. The results of our proposed approach have shown that the multi-classifier fusion has improved the detection of suicide-related text and, also, that Support Vector Machine has shown some promising results when dealingwith multi-class datasets.

AB - As web data evolves, new technological challenges arise and one of the contributing factors to these challenges is the online social networks. Although they have some benets, their negative impact on vulnerable users such as the spread of suicidal ideation is concerning. As such, it is vital to ne tune the approaches and techniques in order to understand the users and their context for early intervention. Therefore, in this study, we measured the impact of data manipulation and feature extraction, specifically using N-grams, on suicide-related social network text (tweets). We propose a diversified ensemble approach (multi-classifier fusion) to improve the detection of suicide-related text classification. Four machine classifiers were used for the fusion: Support Vector Machine, Random Forest, Naive Bayes and Decision Tree. The results of our proposed approach have shown that the multi-classifier fusion has improved the detection of suicide-related text and, also, that Support Vector Machine has shown some promising results when dealingwith multi-class datasets.

KW - Ensemble Learning

KW - Suicide-related Tweets

KW - Text Classification

U2 - 10.1007/978-3-030-29933-0_26

DO - 10.1007/978-3-030-29933-0_26

M3 - Conference contribution

SN - 978-3-030-29932-3

T3 - Advances in Intelligent Systems and Computing

SP - 307

EP - 318

BT - Advances in Computational Intelligence Systems

A2 - Ju, Zhojie

A2 - Yang, Longzhi

A2 - Yang, Chenguang

A2 - Gegov, Alexander

A2 - Zhou, Dalin

PB - Springer

T2 - 19th UK Workshop on Computational Intelligence

Y2 - 4 September 2019 through 5 September 2019

ER -

Detection of suicidal Twitter posts

Abstract

Publication series

Workshop

Keywords

UN SDGs

Access to Document

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Cite this