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2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2016)
San Francisco, CA, USA
Aug. 18, 2016 to Aug. 21, 2016
ISBN: 978-1-5090-2847-4
pp: 1401-1405
Yasin N. Silva , Arizona State University, Glendale, Arizona, USA
Christopher Rich , Arizona State University, Glendale, Arizona, USA
Jaime Chon , Arizona State University, Glendale, Arizona, USA
Lisa M. Tsosie , Arizona State University, Glendale, Arizona, USA
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.
INDEX TERMS
Facebook, Data structures, Boolean functions, Computational modeling, Monitoring, Feeds
CITATION

Y. N. Silva, C. Rich, J. Chon and L. M. Tsosie, "BullyBlocker: An app to identify cyberbullying in facebook," 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), San Francisco, CA, USA, 2016, pp. 1401-1405.
doi:10.1109/ASONAM.2016.7752430
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