richouzo/hate-speech-detection-survey
Trained Neural Networks (LSTM, HybridCNN/LSTM, PyramidCNN, Transformers, etc.) & comparison for the task of Hate Speech Detection on the OLID Dataset (Tweets).
This project offers a comparative study of different machine learning models for identifying hate speech in social media posts. It takes English tweets as input and determines whether they contain offensive or hateful language. Social media managers, content moderators, or platform safety teams can use this to understand the effectiveness of various algorithms in detecting harmful content.
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Use this if you need to evaluate and compare different advanced natural language processing methods for automatically identifying hate speech in user-generated text, particularly on platforms like Twitter.
Not ideal if you are looking for a ready-to-deploy, out-of-the-box hate speech detection tool without the need for model comparison or research.
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CC0-1.0
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Last pushed
Dec 14, 2021
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