Abstract
This article focuses on cyber security issues related to content. Recent surveys show that youth are being exposed to cyber-aggression at increasing rates, with over 43% of youth reporting in one recent survey that they have been bullied online. While research into this problem has been growing, the research community is hampered by a lack of authentic data for studying communication with and among youth. A large corpus with 800,000 instances of cell phone textual data from youth ages 10–14 has been developed to address this need. This article describes the dataset, as well as plans to enable access to the data while protecting the privacy of the study participants. The results from machine learning experiments for the detection of cyberbullying based on labeled data from several sources, including both SMS and social media messages, are also discussed. These algorithms are shown to be effective at detecting cyberbullying across platforms.
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References
Moessner, C.: Cyberbullying, trends and tudes. http://ncpc.mediaroom.com/download/Trends+%26+Tudes+-+Harris+Interactive.pdf (2019)
Bigelow, J.L., Edwards, A., Edwards, L.: Detecting cyberbullying using latent semantic indexing. In: Proceedings of the First International Workshop on Computational Methods for CyberSafety, pp. 11–14. ACM (2016)
Dadvar, M., de Jong, F., Ordelman, R., Trieschnigg, R.: Improved cyberbullying detection using gender information. In: Proceedings of the Twelfth Dutch-Belgian Information Retrieval Workshop (DIR 2012), pp. 23–25. University of Ghent (2012)
Dinakar, K., Reichart, R., Lieberman, H.: Modeling the detection of textual cyberbullying. In: Social Mobile Web Workshop at International Conference on Weblog and Social Media. AAAI (2011)
Reynolds, K., Kontostathis, A., Edwards, L.: Using machine learning to detect cyberbullying. In: Proceedings of the 2011 10th International Conference on Machine Learning and Applications and Workshops, pp. 241–244. IEEE Computer Society (2011)
Al-Garadi, M.A., Hussain, M.R., Khan, N., Murtaza, G., Nweke, H.F., Ali, I., Mujtaba, G., Chiroma, H., Khattak, H.A., Gani, A.: Predicting cyberbullying on social media in the big data era using machine learning algorithms: review of literature and open challenges. IEEE Access. 7, 70701–70718 (2019)
What is cyberbullying. https://www.stopbullying.gov/cyberbullying/what-is-it/index.html (2018)
Willard, N.E.: Cyberbullying and Cyberthreats: Responding to the Challenge of Online Social Aggression, Threats, and Distress. Research Press, Champaign (2007)
Olweus, D.: Bullying at School: What We Know and What We Can Do. Blackwell, Oxford (1993)
Lenhart, A., Smith, A., Anderson, M., Duggan, M., Perrin, A.: Teens, Technology, and Friendships. Pew Research Center, Washington, DC. http://www.pewinternet.org/files/2015/08/Teens-and-Friendships-FINAL2.pdf (2015)
Edwards, L., Kontostathis, A.: Reclaiming privacy: reconnecting victims of cyberbullying and cyberpredation. In: Proceedings of the Reconciling Privacy with Social Media Workshop, Held in Conjunction with the 2012 ACM Conference on Computer Supported Cooperative Work. ACM (2012)
Galán-García, P., de la Puerta, J.G., Gómez, C.L., Santos, I., Bringas, P.G.: Supervised machine learning for the detection of troll profiles in twitter social network: application to a real case of cyberbullying. In: International Joint Conference SOCO’13-CISIS’13-ICEUTE’13, pp. 419–428. Springer (2014)
Weka 3: data mining software in Java. https://www.cs.waikato.ac.nz/ml/weka/ (2019)
Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
John, G.H., Langley, P.: Estimating continuous distributions in bayesian classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338–345. Morgan Kaufmann, San Mateo (1995)
Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Advances in Kernel Methods - Support Vector Learning, pp. 185–208. MIT Press, Cambridge, MA (1999)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. Parallel distributed processing. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1: Foundations, pp. 318–362. MIT Press, Cambridge (1986)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)
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This material is based upon work supported in part by the National Science Foundation under Grant Nos. 0916152, 1812380 and 1421896. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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Edwards, A., Demoll, D., Edwards, L. (2020). Detecting Cyberbullying Activity Across Platforms. In: Latifi, S. (eds) 17th International Conference on Information Technology–New Generations (ITNG 2020). Advances in Intelligent Systems and Computing, vol 1134. Springer, Cham. https://doi.org/10.1007/978-3-030-43020-7_7
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