Tertiary Cyberbullying Detection Using Machine Learning and Captum-Based Explainability

As social media has grown, some people with biased opinions now use it to spread hateful messages and target others because of their gender, race, or ethnicity. Positive communication in diverse communities can build confidence, but negative remarks can damage a person's image and emotional health. The increasing use of social media shows the need for reliable systems to detect and reduce harmful content. In this study, we utilized classical machine learning methods for cyberbullying detection, applying three word vectorization techniques; TFIDF, Word2Vec (Skip-Gram and CBOW), and FastText. These were combined with five traditional machine learning classifiers. Among all models, Logistic Regression and Support Vector Machine integrated with supervised FastText embeddings achieved the highest accuracy of 0.97. Finally, we used Captum to explain and visualize how the model makes its decisions. This helps people better understand the results and makes the model more transparent.