BullyBlocker: Towards the identification of cyberbullying in social networking sites

Yasin Silva; Christopher Rich; Deborah Hall

doi:10.1109/ASONAM.2016.7752420

BullyBlocker: Towards the identification of cyberbullying in social networking sites

Yasin Silva, Christopher Rich, Deborah Hall

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

12 Scopus citations

Abstract

Cyberbullying is the deliberate use of online digital media to communicate false, embarrassing, or hostile information about another person. It is the most common online risk for adolescents and well over half of young people do not tell their parents when it occurs. While there have been many studies about the nature and prevalence of cyberbullying, there has been relatively less work in the area of automated identification of cyberbullying in social media sites. The focus of our work is to develop an automated model to identify and measure the degree of cyberbullying in social networking sites, and a Facebook app for parents, built on this model, that notifies them when cyberbullying occurs. This paper describes the challenges associated with building a computer model for cyberbullying identification, presents key results from psychology research that can be used in such a model, describes an initial model and mobile app design for cyberbullying identification, and describes key 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	1377-1379
Number of pages	3
ISBN (Electronic)	9781509028467
DOIs	https://doi.org/10.1109/ASONAM.2016.7752420
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.2016.7752420

Cite this

Silva, Y., Rich, C., & Hall, D. (2016). BullyBlocker: Towards the identification of cyberbullying in social networking sites. 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. 1377-1379). [7752420] (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.2016.7752420

BullyBlocker : Towards the identification of cyberbullying in social networking sites. / Silva, Yasin; Rich, Christopher; Hall, Deborah.

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. 1377-1379 7752420 (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 & Hall, D 2016, BullyBlocker: Towards the identification of cyberbullying in social networking sites. 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., 7752420, 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. 1377-1379, 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.2016.7752420

Silva Y, Rich C, Hall D. BullyBlocker: Towards the identification of cyberbullying in social networking sites. 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. 1377-1379. 7752420. (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016). https://doi.org/10.1109/ASONAM.2016.7752420

Silva, Yasin ; Rich, Christopher ; Hall, Deborah. / BullyBlocker : Towards the identification of cyberbullying in social networking sites. 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. 1377-1379 (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016).

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N2 - Cyberbullying is the deliberate use of online digital media to communicate false, embarrassing, or hostile information about another person. It is the most common online risk for adolescents and well over half of young people do not tell their parents when it occurs. While there have been many studies about the nature and prevalence of cyberbullying, there has been relatively less work in the area of automated identification of cyberbullying in social media sites. The focus of our work is to develop an automated model to identify and measure the degree of cyberbullying in social networking sites, and a Facebook app for parents, built on this model, that notifies them when cyberbullying occurs. This paper describes the challenges associated with building a computer model for cyberbullying identification, presents key results from psychology research that can be used in such a model, describes an initial model and mobile app design for cyberbullying identification, and describes key areas of future work to improve upon the initial model.

AB - Cyberbullying is the deliberate use of online digital media to communicate false, embarrassing, or hostile information about another person. It is the most common online risk for adolescents and well over half of young people do not tell their parents when it occurs. While there have been many studies about the nature and prevalence of cyberbullying, there has been relatively less work in the area of automated identification of cyberbullying in social media sites. The focus of our work is to develop an automated model to identify and measure the degree of cyberbullying in social networking sites, and a Facebook app for parents, built on this model, that notifies them when cyberbullying occurs. This paper describes the challenges associated with building a computer model for cyberbullying identification, presents key results from psychology research that can be used in such a model, describes an initial model and mobile app design for cyberbullying identification, and describes key areas of future work to improve upon the initial model.

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BullyBlocker: Towards the identification of cyberbullying in social networking sites

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Keywords

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