Detecting Cyberbullying Using ML

Rajam nath

Abstract


Cyberbullying is when someone is bullied via technology. Despite the fact that this has been a concern for some time, there has lately been an upsurge in the awareness of its effects on young people. Young people who use social networking sites, such as MySpace or Facebook, are more likely to be targeted by bullies. For example, by utilizing machine learning, we can identify bullying-related linguistic patterns, which may be utilized to automatically detect cyberbullying content. We gathered the data for our study from the question-and-answer website Formspring. me, which has a high percentage of bullying content. Amazo, a web service, was utilized to label the data.

References


N. E. Willard, "Cyberbullying and Cyberthreats: Responding to the Challenge of Online Social Aggression Threats and Distress" in , Research Press, 2007.

D. Maher, "Cyberbullying: an Ethnographic Case Study of one Australian Upper Primary School Class", Youth Studies Australia, vol. 27, no. 4, pp. 50-57, 2008.

D. Yin, Z. Xue, L. Hong, B. D. Davison, A. Kontostathis and L. Ed-wards, "Detection of Harassment on Web 2.0", Proc. Content Analysis of Web 2.0 Workshop, 2009.

Liu, M. C., Chen, L. C., and Tung, S. C., "The implementation and study of the Web-based thematic learning model," Proceeding of the 6 Global Chinese Conference on Computers in Education, Beijing, 2002.

J. Yangqing, S. Evan, D. Jeff, K. Sergey, L. Jonathan, G. Ross et al., "Caffe: Convolutional Architecture for Fast Feature Embedding", Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675-678, 2014.

P. Fabian, V. Gaël, G. Alexandre, M. Vincent, T. Bertrand, G. Olivier et al., "Scikit-learn: Machine Learning in Python", Journal of Machine Learning Research, pp. 2825-2830, 2011.

K. M. Lee, K. Y. Kim and J. S. Yoo, "Autonomicity Levels and Requirements for Automated Machine Learning", Proceedings of the International Conference on Research in Adaptive and Convergent Systems, pp. 46-48, 2017, Sept.

Gupta, K., & Jiwani, N. (2021). A systematic Overview of Fundamentals and Methods of Business Intelligence. International Journal of Sustainable Development in Computing Science, 3(3), 31-46.


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