Securing Social Spaces: Harnessing Deep Learning to Eradicate
  Cyberbullying
Rohan Biswas Kasturi Ganguly Arijit Das Diganta Saha
In today's digital world, cyberbullying is a serious problem that can harm
the mental and physical health of people who use social media. This paper
explains just how serious cyberbullying is and how it really affects
indi-viduals exposed to it. It also stresses how important it is to find better
ways to detect cyberbullying so that online spaces can be safer. Plus, it talks
about how making more accurate tools to spot cyberbullying will be really
helpful in the future. Our paper introduces a deep learning-based ap-proach,
primarily employing BERT and BiLSTM architectures, to effective-ly address
cyberbullying. This approach is designed to analyse large vol-umes of posts and
predict potential instances of cyberbullying in online spaces. Our results
demonstrate the superiority of the hateBERT model, an extension of BERT focused
on hate speech detection, among the five mod-els, achieving an accuracy rate of
89.16%. This research is a significant con-tribution to "Computational
Intelligence for Social Transformation," prom-ising a safer and more inclusive
digital landscape.