Browsing by Author "Perera, M. V. V."

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Detection of Cyberbullying to Reduce Mental Health Problems using Machine Learning Algorithms
(Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Perera, M. V. V.; Piyumal, K. M.
Social networks and other online platform services are where people are more likely to experience issues with cyberbullying, including kids, young and older adults who are addicted to them. Cyberbullying is an activity that takes place on digital platforms where victims are threatened or bullied individually or in groups by messages or comments online. Various cyberbullying detection techniques are continuously used on social media platforms; however, not all online platform services follow those mechanisms, which may lead to psychological problems that can cause depression and even suicide because people are unaware of taking action to prevent it. Many past cyberbullying detection studies used small datasets and omitted disclosing the total number of features used to train the model. To fill this gap, this study explores how model performance changes with the feature count and what happens when the dataset size increases. Therefore, two cyberbullying datasets with a combined total of 47,183 and 120,556 records were used, containing suspicious activities on Twitter and Facebook that most commonly belong to the cyberbullying category. To compare performance metrics of each model, three methods for feature extraction and three classifiers were used, namely Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM). The highest accuracy for the models created utilizing 47,183 data under the three feature extraction approaches was 94.43%, while the highest accuracy for the 120,556 data was 89.96%.