Development of machine learning integrated cyberbullying response framework for tertiary institutions
| dc.contributor.author | Torome, Alahai Ahmed | |
| dc.date.accessioned | 2026-01-27T09:48:56Z | |
| dc.date.available | 2026-01-27T09:48:56Z | |
| dc.date.issued | 2025-10-27 | |
| dc.description | A dissertation submitted to the faculty of technoscience in partial fulfillment of the requirements for the award of the degree of master of science in computer science of Muni University | |
| dc.description.abstract | With the extensive availability of online learning and digital interactions that have arisen in tertiary institutions since the post-COVID-19 period, this study addresses the growing problem of cyberbullying among students in Arua tertiary institutions entitled as “Development of a Machine Learning Integrated Cyberbullying Response Framework for Tertiary Institutions”. The research is on Muni University, Uganda National Institute for Teacher Education (UNITE), and Arua School of Comprehensive Nursing. The study was based on Ecological Systems Theory and Social Learning Theory and described environmental influences and behaviors that lead to cyberbullying. It identifies underlying drivers including the proliferation of smartphone and social media use, absence of cyber safety policies, peer pressure, online anonymity and limited institutional responsiveness. Cyberbullying on a national level impact approximately 42% of students in Uganda, with harassment, impersonation, and identity-based attacks being the most prevalent — particularly against marginalized groups, like LGBTQ+ students. A mixed-methods design that included surveys for quantitative insights, interviews/focus group discussions for qualitative insights, and machine learning approaches such as K-Means clustering, the Elbow Method, and classifiers such as Random Forests and Decision Trees was utilized. The investigation involved 500 participants (students, staff, and administrators) selected via a multistage sampling process. Main Findings revealed that 60.9% of students had experienced cyberbullying, namely via social media. LGBTQ+ students were significantly more likely to fall victim. It showed through quantitative analysis that low awareness, limited resources, and psychological impacts were three major predictors of cyberbullying. The K-Means model classifies students by their risk levels based on both institutional backing and lived experiences, providing the foundation for an automated response mechanism. The research finds that cyberbullying is rife and more frequently affects minority groups than other classes. Institutional policy failures, lack of support and student ignorance were the major factors that contributed to the discrepancy. The machine learning inspired framework showed promising real-time risk detection potential. Propositions for the use of AI across organizational functions such as integrating the system, regular retraining, broadening the behavioral input variables, training the staff to interpret the outputs effectively and ethical use of the AI were presented. The study also suggests that research using natural language processing and long-term tracking could help strengthen interventions and protect students even better | |
| dc.identifier.citation | Torome, A. A. (2025). Development of machine learning integrated cyberbullying response framework for tertiary institutions (Unpublished graduate dissertation). Muni University, Arua, Uganda | |
| dc.identifier.uri | https://dir.muni.ac.ug/handle/20.500.12260/892 | |
| dc.language.iso | en | |
| dc.publisher | Muni University | |
| dc.subject | Machine learning | |
| dc.subject | integrated cyberbullying | |
| dc.subject | Tertiary institutions | |
| dc.title | Development of machine learning integrated cyberbullying response framework for tertiary institutions |