A framework for early detection of antisocial behavior on Twitter using natural language processing

Ravinder Singh; Jiahua Du; Yanchun Zhang; Hua Wang; Yuan Miao; Omid Ameri Sianaki; Anwaar Ul-Haq

A framework for early detection of antisocial behavior on Twitter using natural language processing

Ravinder Singh, Jiahua Du, Yanchun Zhang, Hua Wang, Yuan Miao, Omid Ameri Sianaki, Anwaar Ul-Haq

Research output: Book chapter/Published conference paper › Conference paper › peer-review

Abstract

Online antisocial behavior is a social problem and a public health threat. A manifestation of such behavior may be fun for a perpetrator, however, can drive a victim into depression, self-confinement, low self-esteem, anxiety,anger, and suicidal ideation. Online platforms such as Twitter and Facebook can sometimes become breeding grounds for such behavior. These platforms may have measures in place to deter online antisocial behavior, however, such behavior still prevails. Most of the measures rely on users reporting to platforms for intervention. In this paper, we advocate a more proactive approach based on natural language processing and machine learning that can enable online platforms to actively look for signs of antisocial behavior and intervene before it gets out of control. By actively searching for such behavior, social media sites can possibly prevent dire situations that can lead to someone committing suicide.

Original language	English
Title of host publication	Complex, Intelligent, and Software Intensive Systems
Subtitle of host publication	Proceedings of the 13th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2019)
Editors	Leonard Barolli, Farookh Khadeer Hussain, Makoto Ikeda
Place of Publication	Switzerland
Publisher	Springer Nature
Pages	484-495
Number of pages	12
Volume	993
ISBN (Electronic)	9783030223540
ISBN (Print)	9783030223533
Publication status	Published - 21 Jun 2019
Event	13th International Conference on Complex, Intelligent, and Software Intensive Systems : CISIS 2019 - University of Sydney, Sydney, Australia Duration: 03 Jul 2019 → 05 Jul 2019 http://voyager.ce.fit.ac.jp/conf/cisis/2019/index.php

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	993

Conference

Conference	13th International Conference on Complex, Intelligent, and Software Intensive Systems
Country/Territory	Australia
City	Sydney
Period	03/07/19 → 05/07/19
Internet address	http://voyager.ce.fit.ac.jp/conf/cisis/2019/index.php

UN SDGs

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

Cite this

Singh, R., Du, J., Zhang, Y., Wang, H., Miao, Y., Ameri Sianaki, O., & Ul-Haq, A. (2019). A framework for early detection of antisocial behavior on Twitter using natural language processing. In L. Barolli, F. Khadeer Hussain, & M. Ikeda (Eds.), Complex, Intelligent, and Software Intensive Systems: Proceedings of the 13th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2019) (Vol. 993, pp. 484-495). (Advances in Intelligent Systems and Computing; Vol. 993). Springer Nature.

Singh, Ravinder ; Du, Jiahua ; Zhang, Yanchun ; Wang, Hua ; Miao, Yuan ; Ameri Sianaki, Omid ; Ul-Haq, Anwaar. / A framework for early detection of antisocial behavior on Twitter using natural language processing. Complex, Intelligent, and Software Intensive Systems: Proceedings of the 13th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2019). editor / Leonard Barolli ; Farookh Khadeer Hussain ; Makoto Ikeda. Vol. 993 Switzerland : Springer Nature, 2019. pp. 484-495 (Advances in Intelligent Systems and Computing).

@inproceedings{2e7c0b52750448b48baa7577966da13f,

title = "A framework for early detection of antisocial behavior on Twitter using natural language processing",

abstract = "Online antisocial behavior is a social problem and a public health threat. A manifestation of such behavior may be fun for a perpetrator, however, can drive a victim into depression, self-confinement, low self-esteem, anxiety,anger, and suicidal ideation. Online platforms such as Twitter and Facebook can sometimes become breeding grounds for such behavior. These platforms may have measures in place to deter online antisocial behavior, however, such behavior still prevails. Most of the measures rely on users reporting to platforms for intervention. In this paper, we advocate a more proactive approach based on natural language processing and machine learning that can enable online platforms to actively look for signs of antisocial behavior and intervene before it gets out of control. By actively searching for such behavior, social media sites can possibly prevent dire situations that can lead to someone committing suicide.",

author = "Ravinder Singh and Jiahua Du and Yanchun Zhang and Hua Wang and Yuan Miao and {Ameri Sianaki}, Omid and Anwaar Ul-Haq",

year = "2019",

month = jun,

day = "21",

language = "English",

isbn = "9783030223533",

volume = "993",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Nature",

pages = "484--495",

editor = "Leonard Barolli and {Khadeer Hussain}, Farookh and Makoto Ikeda",

booktitle = "Complex, Intelligent, and Software Intensive Systems",

address = "United States",

note = "13th International Conference on Complex, Intelligent, and Software Intensive Systems : CISIS 2019 ; Conference date: 03-07-2019 Through 05-07-2019",

url = "http://voyager.ce.fit.ac.jp/conf/cisis/2019/index.php",

}

Singh, R, Du, J, Zhang, Y, Wang, H, Miao, Y, Ameri Sianaki, O & Ul-Haq, A 2019, A framework for early detection of antisocial behavior on Twitter using natural language processing. in L Barolli, F Khadeer Hussain & M Ikeda (eds), Complex, Intelligent, and Software Intensive Systems: Proceedings of the 13th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2019). vol. 993, Advances in Intelligent Systems and Computing, vol. 993, Springer Nature, Switzerland, pp. 484-495, 13th International Conference on Complex, Intelligent, and Software Intensive Systems , Sydney, New South Wales, Australia, 03/07/19.

A framework for early detection of antisocial behavior on Twitter using natural language processing. / Singh, Ravinder; Du, Jiahua; Zhang, Yanchun; Wang, Hua; Miao, Yuan; Ameri Sianaki, Omid; Ul-Haq, Anwaar.

Complex, Intelligent, and Software Intensive Systems: Proceedings of the 13th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2019). ed. / Leonard Barolli; Farookh Khadeer Hussain; Makoto Ikeda. Vol. 993 Switzerland : Springer Nature, 2019. p. 484-495 (Advances in Intelligent Systems and Computing; Vol. 993).

Research output: Book chapter/Published conference paper › Conference paper › peer-review

TY - GEN

T1 - A framework for early detection of antisocial behavior on Twitter using natural language processing

AU - Singh, Ravinder

AU - Du, Jiahua

AU - Zhang, Yanchun

AU - Wang, Hua

AU - Miao, Yuan

AU - Ameri Sianaki, Omid

AU - Ul-Haq, Anwaar

PY - 2019/6/21

Y1 - 2019/6/21

N2 - Online antisocial behavior is a social problem and a public health threat. A manifestation of such behavior may be fun for a perpetrator, however, can drive a victim into depression, self-confinement, low self-esteem, anxiety,anger, and suicidal ideation. Online platforms such as Twitter and Facebook can sometimes become breeding grounds for such behavior. These platforms may have measures in place to deter online antisocial behavior, however, such behavior still prevails. Most of the measures rely on users reporting to platforms for intervention. In this paper, we advocate a more proactive approach based on natural language processing and machine learning that can enable online platforms to actively look for signs of antisocial behavior and intervene before it gets out of control. By actively searching for such behavior, social media sites can possibly prevent dire situations that can lead to someone committing suicide.

AB - Online antisocial behavior is a social problem and a public health threat. A manifestation of such behavior may be fun for a perpetrator, however, can drive a victim into depression, self-confinement, low self-esteem, anxiety,anger, and suicidal ideation. Online platforms such as Twitter and Facebook can sometimes become breeding grounds for such behavior. These platforms may have measures in place to deter online antisocial behavior, however, such behavior still prevails. Most of the measures rely on users reporting to platforms for intervention. In this paper, we advocate a more proactive approach based on natural language processing and machine learning that can enable online platforms to actively look for signs of antisocial behavior and intervene before it gets out of control. By actively searching for such behavior, social media sites can possibly prevent dire situations that can lead to someone committing suicide.

UR - https://www.springer.com/gp/book/9783030223533

M3 - Conference paper

SN - 9783030223533

VL - 993

T3 - Advances in Intelligent Systems and Computing

SP - 484

EP - 495

BT - Complex, Intelligent, and Software Intensive Systems

A2 - Barolli, Leonard

A2 - Khadeer Hussain, Farookh

A2 - Ikeda, Makoto

PB - Springer Nature

CY - Switzerland

T2 - 13th International Conference on Complex, Intelligent, and Software Intensive Systems

Y2 - 3 July 2019 through 5 July 2019

ER -

Singh R, Du J, Zhang Y, Wang H, Miao Y, Ameri Sianaki O et al. A framework for early detection of antisocial behavior on Twitter using natural language processing. In Barolli L, Khadeer Hussain F, Ikeda M, editors, Complex, Intelligent, and Software Intensive Systems: Proceedings of the 13th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2019). Vol. 993. Switzerland: Springer Nature. 2019. p. 484-495. (Advances in Intelligent Systems and Computing).

A framework for early detection of antisocial behavior on Twitter using natural language processing

Abstract

Publication series

Conference

UN SDGs

Other files and links

Fingerprint

Cite this