Revolutionizing Cyberbullying Prevention: A Cutting-Edge Natural Language Processing-Based Approach
Tarek Kanan, Samar Hendawi, Shadi Mahmoud Faleh AlZu’bi, Mohammed W. Elbes, Ala’ S Mughaid · 2023
The rise of social media has brought about new cybersecurity threats that can affect individuals and organizations. To ensure safe social media usage, there is a need for effective cybersecurity applications that can mitigate these threats. Cyberbullying is a serious issue that affects many individuals, particularly young people, and can have long-lasting effects on mental health and well-being. Natural language processing (NLP) techniques can be used to detect cyberbullying in online content. This study utilized the cyberbullying_tweets.csv dataset from Kaggle and implemented two classifiers, Logistic Regression and Decision Tree, to detect cyberbullying in tweets. The results showed that Logistic Regression achieved an accuracy of 91%, while Decision Tree achieved an accuracy of 89%. These findings highlight the potential of NLP techniques to accurately detect cyberbullying and help prevent this harmful behavior on social media platforms.