Cyberbullying Detection in Social Networks: Artificial Intelligence Approach

Authors

  • Nureni Ayofe Azeez Department of Computer Sciences, University of Lagos, Nigeria https://orcid.org/0000-0002-1475-2612
  • Sunday O. Idiakose Department of Computer Sciences, University of Lagos, Nigeria
  • Chinazo Juliet Onyema Department of Computer Science, Federal University of Technology, Owerri
  • Charles Van Der Vyver School of Computer Science and Information Systems, North-West University, Vanderbijlpark Campus, South Africa

DOI:

https://doi.org/10.13052/jcsm2245-1439.1046

Keywords:

Cyberbullying, machine learning, detection, algorithms, twitter, cybercrime, social media

Abstract

Over the past decade, digital communication has reached a massive scale globally. Unfortunately, cyberbullying has become prevalent, with perpetrators hiding behind the mask of relative internet anonymity. In this work, efforts were made to review prominent classification algorithms and also to propose an ensemble model for identifying cases of cyberbullying, using Twitter datasets. The algorithms used for evaluation are Naive Bayes, K-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, Linear Support Vector Classifier, Adaptive Boosting, Stochastic Gradient Descent and Bagging classifiers. Through experimentations, comparisons were made with the classifiers against four metrics: accuracy, precision, recall and F1 score. The results reveal the performances of all the algorithms used with their corresponding metrics. The ensemble model generated better results while Linear Support Vector Classifier (SVC) was the least effective of all. Random Forest classifier has shown to be the best performing classifier with medians of 0.77, 0.73 and 0.94 across the datasets. The ensemble model has shown to improve the results of its constituent classifiers with medians of 0.77, 0.66 and 0.94, as against the 0.59, 0.42 and 0.86 of Linear Support Vector Classifier.

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Author Biographies

Nureni Ayofe Azeez, Department of Computer Sciences, University of Lagos, Nigeria

Nureni Ayofe Azeez obtained his B.Tech. (Hons.) from the Federal University of Technology, Akure, Nigeria in 2005, MSc from the University of Ibadan, Oyo State, Nigeria in 2008, and Ph.D. from University of the Western Cape, South Africa in 2013, all in Computer Science. His areas of research include Security & Privacy, Access Control, Grid and Cloud Computing, Sensor Networks, E-Health and ICT4D. He is a recipient of The Young Scientist Award at the 22nd International CODATA Conference that was held in Cape Town, South Africa in October 2010. He is currently a Senior Lecturer in the Department of Computer Sciences, University of Lagos, Nigeria.

Sunday O. Idiakose, Department of Computer Sciences, University of Lagos, Nigeria

Sunday O. Idiakose graduated with a B.Sc. (Hons) in Computer Science with Second Class Upper Division from the University of Benin, Edo State, Nigeria in 2015. He observed his mandatory National Youth Service Corps (NYSC) programme in Imeko, Ogun State between 2016 and 2017. He has recently defended his M.Sc. programme thesis in Computer Science at the University of Lagos, Lagos, Nigeria. His area of interests include cloud computing, cyber security and distributed systems.

Chinazo Juliet Onyema, Department of Computer Science, Federal University of Technology, Owerri

Chinazo Juliet Onyema earned a (B.Tech) (honour) degree in Mathematics and Computer Science from the Federal University of Technology Owerri (FUTO), Nigeria and Masters (MSc) degree in Computer Science from the University of Lagos, Nigeria. She is currently an assistant lecturer in the Department of Computer Science, Federal University of Technology Owerri (FUTO), Nigeria. Her research interests include Cyber Security and Internet of Things (IoT). She is a member of Nigeria Computer Society (NCS).

Charles Van Der Vyver, School of Computer Science and Information Systems, North-West University, Vanderbijlpark Campus, South Africa

Charles Van Der Vyver obtained his B.Sc. from the Potchefstroom University for Christian Higher Education, Vanderbijlpark, South Africa in 2003, B.Sc. Hons in 2004, M.Sc. in 2007 and Ph.D. in 2011, all from the North-West University, Vanderbijlpark, South Africa, all in Computer Science. His areas of research include Security & Privacy, Water Poverty and Water Management. He is a recipient of a best paper award in 2015 in Kuala Lumpur, Malaysia. He delivered the keynote address during a conference in London, United Kingdom in 2019. He is the recipient of several Faculty and institutional research awards. He is currently a Senior Lecturer in the School of Computer Science and Information Systems, Faculty of Natural- and Agricultural Sciences, North-West University, Vanderbijlpark, South Africa.

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Published

2021-06-21

How to Cite

1.
Azeez NA, Idiakose SO, Onyema CJ, Vyver CVD. Cyberbullying Detection in Social Networks: Artificial Intelligence Approach. JCSANDM [Internet]. 2021 Jun. 21 [cited 2024 Nov. 22];10(4):745-74. Available from: https://journals.riverpublishers.com/index.php/JCSANDM/article/view/6425

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Articles