Abstract:
Cyberbullying has become a significant issue in recent years, particularly among young
people, and there is a need for practical tools to detect and prevent it. Cyberbullying is
sending offensive, abusive or threatening messages to insult a person. It is more dangerous
than traditional bullying because it can occur at any time and from any location and be
done anonymously. Social media is getting vast amounts of data every day. We see in
current trends that Cyberbullying is a bigger and bigger problem. It is even more severe
than regular bullying’s on the internet, like Facebook, Twitter, or other internet platforms.
Finding a bully is far more challenging. So, we collect a data tool that essentially interacts
and communicates with various social media site data using vest technologies like natural
language processing and machine learning that automatically detect bullying. The study
employs various machine-learning algorithms to develop a model for detecting
cyberbullying sentences in Bengali text also discusses the challenges faced in developing
a machine-learning model for Bengali cyberbullying detection and the potential solutions.
Overall, the study demonstrates the potential of machine learning for detecting
cyberbullying in Bengali and contributes to developing practical tools to prevent and
combat cyberbullying in the Bengali-speaking community. This paper proposes an
approach to detect cyberbullying in Bangla sentences using social media datasets and
machine learning techniques, and the evaluation dataset shows that Multinomial Naïve
Bayes performs better and achieves an accuracy of 79.49%.
