Abstract

Hate speech detection is a critical aspect of online content moderation, ensuring that digital platforms remain safe and inclusive. With the exponential rise of social media, harmful content such as hate speech and offensive language has increased, necessitating automated solutions for effective moderation. This project employs Natural Language Processing (NLP) and Machine Learning (ML) techniques to classify tweets into three categories: Hate Speech, Offensive Speech, and No Hate or Offensive Speech. By leveraging a Decision Tree Classifier, the system efficiently detects and categorizes harmful content while reducing manual intervention. The methodology involves data preprocessing, feature extraction using CountVectorizer, and training a classification model to achieve high accuracy. The proposed system overcomes the limitations of traditional keyword-based filtering by improving context awareness and scalability. The implementation is designed to process large volumes of data, making it highly suitable for real-world applications. This approach enhances digital safety, minimizes human effort in moderation, and ensures compliance with ethical standards. Future improvements may include the integration of deep learning models like LSTMs or Transformers and real-time social media API monitoring to enhance accuracy further. This project contributes to the growing need for robust and automated hate speech detection solutions in the digital era.