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BullyBlocker: toward an interdisciplinary approach to identify cyberbullying

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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, 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.

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References

  • Baldry AC, Farrington DP, Sorrentino A (2016) Cyberbullying in youth: a pattern of disruptive behaviour. Psicología Educativa 22(1):19–26

    Article  Google Scholar 

  • Beyond Bullying Website. http://wwwbeyondbullying.com/racistbullying.html

  • Bonnet DG (2007) Transforming odds ratios into correlations for meta-analytic research. Am Psychol 62(3):254–255

    Article  Google Scholar 

  • BullyBlocker App in the Apple Store. Apphttps://itunes.apple.com/us/app/bullyblocker-app/id1236410370?mt=8

  • BullyBlocker Project Website. https://bullyblocker.project.asu.edu/data

  • Dinakar K, Reichart R, Lieberman H (2011) Modeling the Detection of Textual Cyberbullying. In: The international AAAI conference on web and social media (ICWSM)

  • Dooley JJ, Pyżalski J, Cross D (2009) Cyberbullying versus face-to-face bullying: a theoretical and conceptual review. J Psychol 217(4):182–188

    Google Scholar 

  • Drug Abuse Website. http://www.teens.drugabuse.gov/blog/post/four-things-know-about-cyberbullying

  • Drug Rehab Website. http://www.drugrehab.com/guides/bullying

  • Fedewa AL, Ahn S (2011) The effects of bullying and peer victimization on sexual-minority and heterosexual youths: a quantitative meta-analysis of the literature. J GLBT Family Stud 7:398–418

    Article  Google Scholar 

  • Golbeck J (2013) Analyzing the social web. Morgan Kaufmann, Burlington

    Google Scholar 

  • Guo S (2016) A meta-analysis of the predictors of cyberbullying perpetration and victimization. Psychol Sch 53(4):432–453

    Article  Google Scholar 

  • Help your Teen Now Website. http://www.helpyourteennow.com/cyber-bullying-and-addiction-in-teenagers

  • Hinduja S, Patchin JW (2008) Cyberbullying: an exploratory analysis of factors related to offending and victimization. Deviant Behavior 29(2):129–156

    Article  Google Scholar 

  • Hinduja S, Patchin JW (2013) Social influences on cyberbullying behaviors among middle and high school students. J Youth Adolesc 42(5):711–722

    Article  Google Scholar 

  • Hosseinmardi H, Rafiq RI, Han R, Lv Q, Mishra S (2016) Prediction of cyberbullying incidents in a media-based social network. In: The IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM)

  • http://www.bullyingstatistics.org/content/cyber-bullying-statistics.html

  • http://www.pewinternet.org/2015/04/09/teens-social-media-technology-2015/

  • Huang Q, Singh VK, Atrey PK (2014) Cyber bullying detection using social and textual analysis. In: The 3rd international workshop on socially-aware multimedia (SAM)

  • Kelleher JD, Namee BM, D’Arcy A (2015) Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies, 1st edn. The MIT Press, Cambridge

    MATH  Google Scholar 

  • Kowalski RM, Limber SP (2007) Electronic bullying among middle school students. J Adolesc Health 41(6):S22–S30

    Article  Google Scholar 

  • Kowalski RK, Giumetti GW, Schroeder AN, Lattanner MR (2014) Bullying in the digital age: a critical review and meta-analysis of cyberbullying research among youth. Psychol Bull 140(4):1073–1137

    Article  Google Scholar 

  • Mishna F, Khoury-Kassabri M, Gadalla T, Daciuk J (2012) Risk factors for involvement in cyber bullying: victims, bullies and bully–victims. Child Youth Serv Rev 34(1):63–70

    Article  Google Scholar 

  • Morstatter F, Pfeffer J, Liu H, Carley KM (2013) Is the sample good enough? comparing data from Twitter’s streaming API with Twitter’s Firehose. In: Proceedings of the 7th international AAAI conference on web and social media (ICWSM)

  • Nand P, Perera R, Kasture A (2016) How bullying is this message?: a psychometric thermometer for bullying. In: COLING, pp 695–706

  • Ortega R, Elipe P, Mora-Merchin JA, Calmaestra J, Vega E (2009) The emotional impact on victims of traditional bullying and cyberbullying: a study of Spanish adolescents. J Psychol 217(4):197–204

    Google Scholar 

  • Patchin JW, Hinduja S (2012) Cyberbullying—an update and synthesis of the research. In: Patchin JW, Hindija S (eds) Cyberbullying prevention and response. Routledge, London, pp 13–35

    Chapter  Google Scholar 

  • Piazza J, Bering JM (2009) Evolutionary cyber-psychology: applying an evolutionary framework to Internet behavior. Comput Hum Behav 25(6):1258–1269

    Article  Google Scholar 

  • Rafiq RI, Hosseinmardi H, Han R, Lv Q, Mishra S, Arredondo-Mattson S (2015) Careful what you share in six seconds: detecting cyberbullying instances in Vine. In: The IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM)

  • Reynolds K, Kontostathis A, Edwards L (2011) Using Machine Learning to Detect Cyberbullying. In: The 10th international conference on machine learning and applications and workshops (ICMLA)

  • Silva YN, Pearson S, Cheney JA (2013) Database Similarity Join for Metric Spaces. The International Conference on Similarity Search and Applications (SISAP), vol 8199. Springer LNCS, pp 266–279

  • Silva YN, Pearson SS, Chon J, Roberts R (2015) Similarity Joins: Their implementation and interactions with other database operators. Inf Syst 52:149–162

    Article  Google Scholar 

  • Silva YN, Rich C, Hall D (2016) BullyBlocker: towards the identification of cyberbullying in social networking sites. In: The IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), 2016, pp 1377–1379

  • Squicciarini A, Rajtmajer S, Liu Y, Griffin C (2015) Identification and characterization of cyberbullying dynamics in an online social network. In: The IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM)

  • Tokunaga RS (2010) Following you home from school: a critical review and synthesis of research on cyberbullying victimization. Comput Hum Behav 26(3):277–287

    Article  Google Scholar 

  • Van Geel M, Vedder P, Tanilon J (2014) Relationship between peer victimization, cyberbullying, and suicide in children and adolescents: a meta-analysis. JAMA Pediatr 168(5):435–442

    Article  Google Scholar 

  • Williams KR, Guerra NG (2007) Prevalence and predictors of internet bullying. J Adolesc Health 41(6):S14–S21

    Article  Google Scholar 

  • Wolke D, Lereya T, Tippett N (2016) Individual and social determinants of bullying and cyberbullying. In: Vollink T, Dehue F, Guckin C (eds) Cyberbullying. Routledge, London, pp 26–53

    Google Scholar 

  • Xu J-M, Jun K-S, Zhu X, Bellmore A (2012) Learning from bullying traces in social media. In: Proceedings of the 2012 conference of the North American chapter of the association for computational linguistics: Human language technologies. Association for Computational Linguistics, pp 656–666

  • Ybarra ML, Mitchell KJ (2008) How risky are social networking sites? a comparison of places online where youth sexual solicitation and harassment occurs. Pediatrics 121(2):e350–e357

    Article  Google Scholar 

  • Tang M, Tahboub RY, Aref WG, Atallah MJ, Malluhi QM, Ouzzani M, Silva YN (2016) Similarity Group-by Operators for Multi-dimensional Relational Data. IEEE Trans Knowl Data Eng 28(2):510–523

  • Yu C, Cui B, Wang S, Su J (2007) Efficient index-based knn join processing for high-dimensional data. Inf Softw Technol 49:332–344

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Acknowledgement

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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Authors and Affiliations

  1. Arizona State University, Glendale, AZ, USA

    Yasin N. Silva, Deborah L. Hall & Christopher Rich

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  1. Yasin N. Silva
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  2. Deborah L. Hall
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  3. Christopher Rich
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Corresponding author

Correspondence to Yasin N. Silva.

Additional information

This work was supported by National Science Foundation Award # 1719722, the Dion Initiative for Child Well-Being and Bullying Prevention and ASU NCUIRE awards.

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Silva, Y.N., Hall, D.L. & Rich, C. BullyBlocker: toward an interdisciplinary approach to identify cyberbullying. Soc. Netw. Anal. Min. 8, 18 (2018). https://doi.org/10.1007/s13278-018-0496-z

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  • DOI: https://doi.org/10.1007/s13278-018-0496-z

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