BullyBlocker: toward an interdisciplinary approach to identify cyberbullying

  • Yasin N. Silva
  • Deborah L. Hall
  • Christopher Rich
Original Article


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, yet 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 have been relatively few in the area of automated identification of cyberbullying that integrate findings from computer science and psychology. The goal of our work is thus to adopt an interdisciplinary approach to develop an automated model for identifying and measuring the degree of cyberbullying in social networking sites, and a Facebook app, built on this model, that notifies parents about the likelihood that their adolescent is a cyberbullying victim. This paper describes the challenges associated with building a computer model for cyberbullying identification, presents key results from psychology research that can be used to inform such a model, introduces a holistic model and mobile app design for cyberbullying identification, presents a novel evaluation framework for assessing the effectiveness of the identification model, and highlights crucial areas of future work. Importantly, the proposed model—which can be applied to other social networking sites—is the first that we know of to bridge computer science and psychology to address this timely problem.


Cyberbullying Automated identification Social networks Facebook Psychology Cyberbullying factors Vulnerability factors 



The authors would like to thank ASU students Lisa Tsosie, Jaime Chon, Tara Tucker, Chance Brown, Liz Garcia, Bryan Sawkins, Rusty Conway, Anthony Nieuwenhuyse, Tom Schenk, Lu Cheng, Ashley Trow, Ayush Sanyal, Linle Jiang, Victoria Delgadillo, and Carmen Sanchez for their contributions to the design and implementation of the BullyBlocker app.


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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Arizona State UniversityGlendaleUSA

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