BullyBlocker: An app to identify cyberbullying in facebook

Yasin Silva; Christopher Rich; Jaime Chon; Lisa M. Tsosie

doi:10.1109/ASONAM.2016.7752430

BullyBlocker: An app to identify cyberbullying in facebook

Yasin Silva, Christopher Rich, Jaime Chon, Lisa M. Tsosie

Arizona State University

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

9 Scopus citations

Abstract

Cyberbullying is the most common online risk for adolescents, and it has been reported that over half of young people do not tell their parents when it occurs. Cyberbullying involves the deliberate use of online digital media to communicate false or embarrassing information about another person. While previous work has extensively analyzed the nature and prevalence of cyberbullying, there has been significantly less work in the area of automated identification of cyberbullying, particularly in social networking sites. The focus of our work is to develop a computational model to identify and measure the intensity of cyberbullying in social networking sites. In this paper, we present and demonstrate BullyBlocker, an app that identifies instances of cyberbullying in Facebook and notifies parents when it occurs. This paper presents the most relevant characteristics of our initial cyberbullying identification model, key app design and implementation details, the demonstration scenarios, and several areas of future work to improve upon the initial model.

Original language	English (US)
Title of host publication	Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
Editors	Ravi Kumar, James Caverlee, Hanghang Tong
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1401-1405
Number of pages	5
ISBN (Electronic)	9781509028467
DOIs	https://doi.org/10.1109/ASONAM.2016.7752430
State	Published - Nov 21 2016
Event	2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 - San Francisco, United States Duration: Aug 18 2016 → Aug 21 2016

Publication series

Name	Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

Other

Other	2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
Country/Territory	United States
City	San Francisco
Period	8/18/16 → 8/21/16

Keywords

Facebook
automated identification
cyberbullying
social networks

ASJC Scopus subject areas

Computer Networks and Communications
Sociology and Political Science
Communication

Access to Document

10.1109/ASONAM.2016.7752430

Cite this

Silva, Y., Rich, C., Chon, J., & Tsosie, L. M. (2016). BullyBlocker: An app to identify cyberbullying in facebook. In R. Kumar, J. Caverlee, & H. Tong (Eds.), Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 (pp. 1401-1405). Article 7752430 (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASONAM.2016.7752430

BullyBlocker: An app to identify cyberbullying in facebook. / Silva, Yasin; Rich, Christopher; Chon, Jaime et al.
Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. ed. / Ravi Kumar; James Caverlee; Hanghang Tong. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1401-1405 7752430 (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016).

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

Silva, Y, Rich, C, Chon, J & Tsosie, LM 2016, BullyBlocker: An app to identify cyberbullying in facebook. in R Kumar, J Caverlee & H Tong (eds), Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016., 7752430, Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, Institute of Electrical and Electronics Engineers Inc., pp. 1401-1405, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, San Francisco, United States, 8/18/16. https://doi.org/10.1109/ASONAM.2016.7752430

Silva Y, Rich C, Chon J, Tsosie LM. BullyBlocker: An app to identify cyberbullying in facebook. In Kumar R, Caverlee J, Tong H, editors, Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1401-1405. 7752430. (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016). doi: 10.1109/ASONAM.2016.7752430

Silva, Yasin ; Rich, Christopher ; Chon, Jaime et al. / BullyBlocker : An app to identify cyberbullying in facebook. Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. editor / Ravi Kumar ; James Caverlee ; Hanghang Tong. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1401-1405 (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016).

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abstract = "Cyberbullying is the most common online risk for adolescents, and it has been reported that over half of young people do not tell their parents when it occurs. Cyberbullying involves the deliberate use of online digital media to communicate false or embarrassing information about another person. While previous work has extensively analyzed the nature and prevalence of cyberbullying, there has been significantly less work in the area of automated identification of cyberbullying, particularly in social networking sites. The focus of our work is to develop a computational model to identify and measure the intensity of cyberbullying in social networking sites. In this paper, we present and demonstrate BullyBlocker, an app that identifies instances of cyberbullying in Facebook and notifies parents when it occurs. This paper presents the most relevant characteristics of our initial cyberbullying identification model, key app design and implementation details, the demonstration scenarios, and several areas of future work to improve upon the initial model.",

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note = "Publisher Copyright: {\textcopyright} 2016 IEEE. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 ; Conference date: 18-08-2016 Through 21-08-2016",

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N2 - Cyberbullying is the most common online risk for adolescents, and it has been reported that over half of young people do not tell their parents when it occurs. Cyberbullying involves the deliberate use of online digital media to communicate false or embarrassing information about another person. While previous work has extensively analyzed the nature and prevalence of cyberbullying, there has been significantly less work in the area of automated identification of cyberbullying, particularly in social networking sites. The focus of our work is to develop a computational model to identify and measure the intensity of cyberbullying in social networking sites. In this paper, we present and demonstrate BullyBlocker, an app that identifies instances of cyberbullying in Facebook and notifies parents when it occurs. This paper presents the most relevant characteristics of our initial cyberbullying identification model, key app design and implementation details, the demonstration scenarios, and several areas of future work to improve upon the initial model.

AB - Cyberbullying is the most common online risk for adolescents, and it has been reported that over half of young people do not tell their parents when it occurs. Cyberbullying involves the deliberate use of online digital media to communicate false or embarrassing information about another person. While previous work has extensively analyzed the nature and prevalence of cyberbullying, there has been significantly less work in the area of automated identification of cyberbullying, particularly in social networking sites. The focus of our work is to develop a computational model to identify and measure the intensity of cyberbullying in social networking sites. In this paper, we present and demonstrate BullyBlocker, an app that identifies instances of cyberbullying in Facebook and notifies parents when it occurs. This paper presents the most relevant characteristics of our initial cyberbullying identification model, key app design and implementation details, the demonstration scenarios, and several areas of future work to improve upon the initial model.

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BullyBlocker: An app to identify cyberbullying in facebook

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Keywords

ASJC Scopus subject areas

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