Abstract
Due to the prevalence and severe consequences of cyberbullying, numerous research works have focused on mining and analyzing social network data to understand cyberbullying behavior and then using the gathered insights to develop accurate classifiers to detect cyberbullying. Some recent works have been proposed to leverage the detection classifiers in a centralized cyberbullying detection system and send notifications to the concerned authority whenever a person is perceived to be victimized. However, two concerns limit the effectiveness of a centralized cyberbullying detection system. First, a centralized detection system gives a uniform severity level of alerts to everyone, even though individual guardians might have different tolerance levels when it comes to what constitutes cyberbullying. Second, the volume of data being generated by old and new social media makes it computationally prohibitive for a centralized cyberbullying detection system to be a viable solution. In this work, we propose BullyAlert, an android mobile application for guardians that allows the computations to be delegated to the hand-held devices. In addition to that, we incorporate an adaptive classification mechanism to accommodate the dynamic tolerance level of guardians when receiving cyberbullying alerts. Finally, we include a preliminary user analysis of guardians and monitored users using the data collected from BullyAlert usage.
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References
https://github.com/RahatIbnRafiq/AndroidCodesForCyberbullying, [Online; Accessed 22 October 2018]
Broderick, R.: 9 teenage suicides in the last year were linked to cyber-bullying on social network ask.fm. http://www.buzzfeed.com/ryanhatesthis/a-ninth-teenager-since-last-september-has-committed-suicide (2013). [Online; Accessed 14 Jan 2014]
Center, C.R.: Cyberbullying research center. http://cyberbullying.us (2013). [Online; Accessed Sep 2013]
Council, N.C.P.: Teens and cyberbullying (2007). executive summary of a report on research conducted for National Crime Prevention Council
Dadvar, M., Trieschnigg, D., Ordelman, R., de Jong, F.: Improving cyberbullying detection with user context. In: Serdyukov, P. (ed.) Advances in Information Retrieval, pp. 693–696. Springer, Berlin Heidelberg, Berlin, Heidelberg (2013)
Dinakar, K., Jones, B., Havasi, C., Lieberman, H., Picard, R.: Common sense reasoning for detection, prevention, and mitigation of cyberbullying (2012). https://doi.org/10.1145/2362394.2362400, http://doi.acm.org/10.1145/2362394.2362400
DiProperzio, L.: Cyberbullying applications. http://www.parents.com/kids/safety/internet/best-apps-prevent-cyberbullying/ (2015). [Online; Accessed 6 Feb 2015]
Menesini, E., Nocentini, A.: Cyberbullying definition and measurement. some critical considerations. J. Psychol. 217(4), 320–323 (2009)
Goldman, R.: Teens indicted after allegedly taunting girl who hanged herself, bbc news. http://abcnews.go.com/Technology/TheLaw/teens-charged-bullying-mass-girl-kill/story?id=10231357 (2010). [Online; Accessed 14 Jan 2014]
Hinduja, S., Patchin, J.W.: Cyberbullying Research Summary, Cyberbullying and Suicide (2010)
Hosseinmardi, H., Ghasemianlangroodi, A., Han, R., Lv, Q., Mishra, S.: Towards understanding cyberbullying behavior in a semi-anonymous social network. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 244–252. IEEE, Beijing, China (2014)
Hosseinmardi, H., Rafiq, R.I., Han, R., Lv, Q., Mishra, S.: Prediction of cyberbullying incidents in a media-based social network. In: Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, IEEE, San Francisco, CA, USA (2016)
Hunter, S.C., Boyle, J.M., Warden, D.: Perceptions and correlates of peer-victimization and bullying. British J. Educ. Psychol. 77(4), 797–810 (2007)
Kontostathis, A., West, W., Garron, A., Reynolds, K., Edwards, L.: Identify predators using chatcoder 2.0. In: CLEF (Online Working Notes/Labs/Workshop) (2012)
Kontostathis, A.: Chatcoder: Toward the tracking and categorization of internet predators. In: Proceedings of Text Mining Workshop 2009 Held in Conjunction with the Ninth SIAM International Conference on Data Mining (SDM 2009). Sparks, NV, May 2009 (2009)
Kowalski, R.M., Limber, S., Limber, S.P., Agatston, P.W.: Cyberbullying: Bullying in the Digital Age. John Wiley & Sons, Reading, MA (2012)
Lepage, E.: Instagram statistics. http://blog.hootsuite.com/instagram-statistics-for-business/ (2015). [Online; Accessed 6 Feb 2015]
Li, H.H.S., Yang, Z., Lv, Q., Han, R.I.R.R., Mishra, S.: A comparison of common users across instagram and ask.fm to better understand cyberbullying. In: 2014 IEEE Fourth International Conference on Big Data and Cloud Computing, pp. 355–362. IEEE, Sydney, Australia (December 2014). https://doi.org/10.1109/BDCloud.2014.87
Mohsin, M.: 10 youtube stats every marketer should know in 2019. https://www.oberlo.com/blog/youtube-statistics (2019). [Online; Accessed 6 Sep 2019]
Nahar, V., Unankard, S., Li, X., Pang, C.: Sentiment analysis for effective detection of cyber bullying. In: Sheng, Q.Z., Wang, G., Jensen, C.S., Xu, G. (eds.) APWeb 2012. LNCS, vol. 7235, pp. 767–774. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29253-8_75
Olweus, D.: Bullying at School: What We Know and What We Can Do (1993)
Patchin, J.W., Hinduja, S.: An update and synthesis of the research. Cyberbullying Prevention and Response: Expert Perspectives, p. 13 (2012)
Python, S.L.: http://scikit-learn.org/stable/modules/sgd.html, [Online; Accessed 22 October 2018]
Rafiq, R.I.: https://github.com/RahatIbnRafiq/cybersafetyapp_servercodes, [Online; Accessed 22 October 2018]
Rafiq, R.I., Hosseinmardi, H., Han, R., Lv, Q., Mishra, S.: Scalable and timely detection of cyberbullying in online social networks. In: Proceedings of the 33rd Annual ACM Symposium on Applied Computing SAC 2018, pp. 1738–1747. ACM, New York, NY, USA (2018). https://doi.org/10.1145/3167132.3167317, http://doi.acm.org/10.1145/3167132.3167317
Rafiq, R.I., Hosseinmardi, H., Han, R., Lv, Q., Mishra, S., Mattson, S.A.: Careful what you share in six seconds: detecting cyberbullying instances in vine. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, pp. 617–622. ACM, Paris, France (2015)
Rajadesingan, A., Zafarani, R., Liu, H.: Sarcasm detection on twitter: a behavioral modeling approach. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining WSDM 2015, pp. 97–106. ACM, New York, NY, USA (2015). https://doi.org/10.1145/2684822.2685316, http://doi.acm.org/10.1145/2684822.2685316
Sanchez, H., Kumar, S.: Twitter bullying detection. In: NSDI, p. 15. USENIX Association, Berkeley, CA, USA (2012)
Smith, P.K., del Barrio, C., Tokunaga, R.: Principles of Cyberbullying Research. Definitions, measures and methodology, Chapter: Definitions of Bullying and Cyberbullying: How Useful Are the Terms? Routledge (2012)
Smith-Spark, L.: Hanna smith suicide fuels calls for action on ask.fm cyberbullying, CNN. http://www.cnn.com/2013/08/07/world/europe/uk-social-media-bullying/ (2013). [Online; Accessed 14 Jan 2014]
Thom, B.: SafeChat: Using Open Source Software to Protect Minors from Internet Predation. Ursinus College (2011). https://books.google.com/books?id=SLbQvQEACAAJ
Ueland, S.: 10 new social networks for 2019. https://www.practicalecommerce.com/10-new-social-networks-for-2019 (2019). [Online; Accessed 6 Sep 2019]
Villatoro-Tello, E., Jurez-Gonzlez, A., Escalante, H.J., Gmez, M.M.Y., Pineda, L.V.: A two-step approach for effective detection of misbehaving users in chats. In: Forner, P., Karlgren, J., Womser-Hacker, C. (eds.) CLEF (Online Working Notes/Labs/Workshop). CEUR Workshop Proceedings, vol. 1178. CEUR-WS.org (2012)
Willard, N.: Cyberbullying and cyberthreats: Responding to the challenge of online social aggression, threats, and distress. Research, Champaign, IL (2007)
Yin, D., Xue, Z., Hong, L., Davison, B.D., Kontostathis, A., Edwards, L.: Detection of harassment on web 2.0. In: Proceedings of the Content Analysis in the WEB 2, 1–7 (2009)
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Rafiq, R.I., Han, R., Lv, Q., Mishra, S. (2020). BullyAlert- A Mobile Application for Adaptive Cyberbullying Detection. In: Liu, J., Gao, H., Yin, Y., Bi, Z. (eds) Mobile Computing, Applications, and Services. MobiCASE 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 341. Springer, Cham. https://doi.org/10.1007/978-3-030-64214-3_1
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