Abstract:

Because of the speedy surge in social media's expansion, the proliferation of malicious and harmful poses a substantial worry in contemporary society. The identification of hate speech on platforms like Twitter is crucial for various tasks such as controversial event extraction, AI chatterbot creation, content suggestions, and sentiment analysis. Researchers have invested considerable effort in addressing the challenging task of identifying hostile content due to the rise in hate speech and harmful information. The objective is to classify tweets as Hateful, Offensive, or neither. However, this task is highly complex due to the intricate nature of natural language constructs, encompassing different manifestations of animosity directed at various demographics, and the multitude of ways the same meaning can be expressed.Previous research has predominantly relied on manual feature extraction or employed representation-learning techniquesfollowed by linear classifiers. Nevertheless, deep learning methods have recently demonstrated significant accuracy improvements in complex problems across speech, vision, and text applications.

Description:

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