Attribute-specific Cyberbullying Detection Using Artificial Intelligence


  • Adeyinka Orelaja

    Department of Computer Science, Austin Peay State University, Tennessee, 37044, USA

  • Chidubem Ejiofor

    Department of Computer Science, Western Illinois University, Macomb, Illinois, 61455, USA

  • Samuel Sarpong

    Department of Computing, East Tennessee State University, Tennessee, 37604, USA

  • Success Imakuh

    Department of Computing, Teesside University, Middlesbrough, TS1 3BX, United Kingdom

  • Christian Bassey

    Department of Computer Science, Innopolis University, Innopolis, 420500, Russia

  • Iheanyichukwu Opara

    Department of Oil and Gas Engineering, Robert Gordon University, Aberdeen, AB10 7AQ, United Kingdom

  • Josiah Nii Armah Tettey

    Department of Computer Science, Wright State University, Dayton, Ohio, 45435, USA

  • Omolola Akinola

    Department of Information System and Analysis, Lamar University, Beaumont, Texas, 77705, USA



Cyberbullying, a pervasive issue in the digital age, poses threats to individuals’ well-being across various attributes such as religion, age, ethnicity, and gender. This research employs artificial intelligence to detect cyberbullying instances in Twitter data, utilizing both traditional and deep learning models. The study repurposes the Sentiment140 dataset, originally intended for sentiment analysis, for the nuanced task of cyberbullying detection. Ethical considerations guide the dataset transformation process, ensuring responsible AI development. The Naive Bayes algorithm demonstrates commendable precision, recall, and accuracy, showcasing its efficacy. The Bi-LSTM model, leveraging deep learning capabilities, exhibits nuanced cyberbullying detection. The study also underscores limitations, emphasizing the need for refined models and diverse datasets.


Cyberbullying detection, Social media analysis, Artificial intelligence, Naive Bayes, Bi-LSTM, Ethical AI, Machine learning, Digital well-being


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How to Cite

Orelaja, A., Ejiofor, C., Sarpong, S., Imakuh, S., Bassey, C., Opara, I., Tettey, J. N. A., & Akinola, O. (2024). Attribute-specific Cyberbullying Detection Using Artificial Intelligence. Journal of Electronic & Information Systems, 6(1), 10–21.