Bilingual Cyber Bullying Detection System: Enhancing Online Safety
Shailaja Nilesh Uke, Kavya Amrutkar, Arpita Kulkarni, Tarun Kasliwal, Aryan Konde · 2024
In this digital era where social media is present everywhere, cyberbullying has emerged as a critical issue. It adversely affects mental health and online interactions. A bilingual cyberbullying detection system has been proposed to address this issue. This system is capable of analyzing text in both English and Hindi (transliterated into English). The system employs advanced data extraction and preprocessing techniques. Leverages machine learning algorithms to detect and accurately classify cyberbullying. The dataset for the English language has been sourced from Twitter while the dataset for Hindi has been created by extracting comments from YouTube. The systems architecture integrates Python technologies, Streamlit, Pandas and Scikit-learn to result in a robust web application designed for real-time text analysis. Regular expressions are used to identify and filter offensive language. Despite obstacles like algorithm bias and data privacy, the system is committed to creating a safer digital environment. This research demonstrates the practical application of these technologies to create a multilingual tool aimed at mitigating cyberbullying on social media platforms.