• Iorliam, A.
  Department of Mathematics & Computer Science, BSU, Makurdi, Nigeria
  https://orcid.org/0000-0001-8238-9686




• Agber, S.
  Department of Mathematics & Computer Science, BSU, Makurdi, Nigeria
  




• Dzungwe, M. P.
  Department of Mathematics & Computer Science, BSU, Makurdi, Nigeria
  




• Kwaghtyo, D. K.
  Department of Mathematics & Computer Science, BSU, Makurdi, Nigeria
  




• Bum, S.
  Department of Mathematics & Computer Science, BSU, Makurdi, Nigeria
  

Social media provides opportunities for individuals to anonymously communicate and express hateful feelings and opinions at the comfort of their rooms. This anonymity has become a shield for many individuals or groups who use social media to express deep hatred for other individuals or groups, tribes or race, religion, gender, as well as belief systems. In this study, a comparative analysis is performed using Long Short-Term Memory and Convolutional Neural Network deep learning techniques for Hate Speech classification. This analysis demonstrates that the Long Short-Term Memory classifier achieved an accuracy of 92.47%, while the Convolutional Neural Network classifier achieved an accuracy of 92.74%. These results showed that deep learning techniques can effectively classify hate speech from normal speech.