Abstract:
This report presents a machine learning and natural language processing (NLP)-based
system for detecting and moderating hate speech and toxicity on online platforms.
With the exponential growth of user-generated content, managing and mitigating toxic
behavior has become a critical challenge to ensure a safer online environment. The
proposed solution leverages advanced NLP techniques and supervised machine
learning models trained on a large, annotated dataset. By employing transformer-
based architectures like BERT, the system is designed to identify hate speech and
toxic language in real time across various social media and communication platforms.
The methodology incorporates data preprocessing, feature extraction, and model
optimization to achieve high accuracy while addressing challenges such as linguistic
ambiguity and context dependency. Experimental results demonstrate the
effectiveness of the system in detecting toxicity with a competitive accuracy rate,
outperforming traditional methods. This work not only highlights the technical
feasibility of automated hate speech detection but also underscores its societal impact
by fostering healthier online interactions. The findings contribute to the ongoing
efforts to combat digital toxicity and can serve as a foundation for further
advancements in this field.
