  Cyberbullying is of extreme prevalence today. Online-hate comments, toxicity,
cyberbullying amongst children and other vulnerable groups are only growing
over online classes, and increased access to social platforms, especially post
COVID-19. It is paramount to detect and ensure minors' safety across social
platforms so that any violence or hate-crime is automatically detected and
strict action is taken against it. In our work, we explore binary
classification by using a combination of datasets from various social media
platforms that cover a wide range of cyberbullying such as sexism, racism,
abusive, and hate-speech. We experiment through multiple models such as
Bi-LSTM, GloVe, state-of-the-art models like BERT, and apply a unique
preprocessing technique by introducing a slang-abusive corpus, achieving a
higher precision in comparison to models without slang preprocessing.
