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Code against Hate: Building a Cyberbullying Detector in Python

Juveria Fathima, T. Aditya Sai Srinivas, M. Bharathi

Abstract


In the digital age, combating cyberbullying is imperative for fostering a safe online environment. This project introduces a Python-based machine learning approach to detect cyberbullying in tweets. Leveraging a dataset featuring "tweet_text" and "cyberbullying_type" labels, the system employs a Multinomial Naive Bayes classifier after transforming text data using TF-IDF vectorization. The model is trained, evaluated, and serves as a vigilant defender against cyber threats, contributing to the ongoing efforts for online civility. By blending technology and ethics, this initiative underscores the role of machine learning in fostering empathy and ensuring a positive digital experience for users.


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References


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