Hate Speech Detection: Leveraging Machine Learning & Deep Learning for A Safer Internet
Keywords:
Machine Learning, Ensemble classifiers, Deep Learning, BERT, Hate Speech detection, Natural Language ProcessingAbstract
This paper proposes and analyses a hate speech detection model using various Machine learning and Deep Learning Techniques for text classification. It introduces an innovative Hate Speech Detection Model designed to address the escalating challenge of hate speech on social media platforms. The surge in online hate poses a significant threat to community well-being, necessitating advanced tools for accurate identification and mitigation. Conventional methods often struggle with the dynamic nature of language, prompting the development of a model that leverages state of-the-art natural language processing techniques and machine learning algorithms. Comparative analyses against existing models underscore its effectiveness. Our Hate Speech Detection Model emerges as a robust and scalable solution to combat online hate, contributing to the creation of a safer and more inclusive digital space for all users. Hate speech detection involves discovering if communication like texts, posts, comments, replies, audio, videos etc. contains hate or if it tries to instigate violence towards a community or a person. This is usually based on prejudice against 'protected characteristics' such as their ethnicity, gender, sexual orientation, religion, age etc. Social media is a platform for communication through which users create online communities to share information, ideas and personal messages in addition to other content. Social media is an essential part of many people’s lives. It is in everything, and it is everywhere. It is omnipresent. But unfortunately, trivial communication between people has now been hijacked and replaced by the ill-humoured, and ill-intentioned culture of trolling, which has become endemic on all social media sites.
In conclusion, our Hate Speech Detection Model stands as a formidable tool in the ongoing battle against online hate. By harnessing the power of advanced natural language processing techniques, the model showcases promising results, providing a scalable solution to address the escalating challenges posed by hate speech. As digital spaces continue to evolve, our model serves as a beacon, promoting inclusivity and fostering a safer online community for all.
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