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Identification and Detection of Cyberbullying on Facebook Using Machine Learning Algorithms

Identification and Detection of Cyberbullying on Facebook Using Machine Learning Algorithms

Nureni Ayofe AZEEZ, Sanjay Misra, Omotola Ifeoluwa LAWAL, Jonathan Oluranti
Copyright: © 2021 |Volume: 23 |Issue: 4 |Pages: 21
ISSN: 1548-7717|EISSN: 1548-7725|EISBN13: 9781799859192|DOI: 10.4018/JCIT.296254
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MLA

Nureni Ayofe AZEEZ, et al. "Identification and Detection of Cyberbullying on Facebook Using Machine Learning Algorithms." JCIT vol.23, no.4 2021: pp.1-21. http://doi.org/10.4018/JCIT.296254

APA

Nureni Ayofe AZEEZ, Misra, S., Omotola Ifeoluwa LAWAL, & Oluranti, J. (2021). Identification and Detection of Cyberbullying on Facebook Using Machine Learning Algorithms. Journal of Cases on Information Technology (JCIT), 23(4), 1-21. http://doi.org/10.4018/JCIT.296254

Chicago

Nureni Ayofe AZEEZ, et al. "Identification and Detection of Cyberbullying on Facebook Using Machine Learning Algorithms," Journal of Cases on Information Technology (JCIT) 23, no.4: 1-21. http://doi.org/10.4018/JCIT.296254

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Abstract

The use of social media platforms such as Facebook, Twitter, Instagram, WhatsApp, etc. have enabled a lot of people to communicate effectively and frequently with each other and this has enabled cyberbullying to occur more frequently while using these networks. Cyberbullying is known to be the cause of some serious health issues among social media users and creating a way to identify and detect this holds significant importance. This paper takes a look at unique features gotten from the Facebook dataset and develops a model that identifies and detect cyberbullying posts by applying machine learning algorithms (Naïve Bayes Algorithm and K-Nearest Neighbor). The project also uses a feature selection algorithm namely x2 test (Chi-Square test) to select important features which can improve the performance of the classifiers and decrease classification time. The result of this paper tends to detect cyberbullying in Facebook with a high degree of accuracy and also improve the performance of the machine learning classifiers.