Abstract
Online social networking platforms have become a common choice for people to communicate with friends, relatives, or business partners. This allows sharing life achievement, success, and many more. In parallel, it also invited hidden issues such as web-spamming, cyberbullying, cybercrime, and others. This paper addresses the issue of cyberbullying with machine learning models. Three classifiers namely naive bayes, logistic regression, and the random forest were used to develop the model. The experimental outcome confirmed that the random forest classifier is a better choice for the said problem which yielded 93% accuracy for the best case.
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Notes
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https://www.statista.com/statistics/617136/digital-population-worldwide/ accessed on 18-02-2020.
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Roy, P.K., Singh, A., Tripathy, A.K., Das, T.K. (2022). Identifying Cyberbullying Post on Social Networking Platform Using Machine Learning Technique. In: Sahoo, J.P., Tripathy, A.K., Mohanty, M., Li, KC., Nayak, A.K. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 302. Springer, Singapore. https://doi.org/10.1007/978-981-16-4807-6_18
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