Combatting Falsehoods and Discriminatory Speech with NLP and ML Techniques
Keywords:
fake news, hate specchAbstract
The rise of fake news and hate speech in the digital era has been a major challenge to the integrity of information and the well-being of society. This paper explores the use of machine learning and natural language processing (NLP) to detect and prevent the spread of false information and discriminatory speech. We review recent advances in NLP and ML algorithms for detecting fake news and hate speech, including text classification, sentiment analysis, and representation learning. Additionally, we present case studies and real-world applications of these techniques, highlighting their strengths and limitations. The goal of this paper is to provide a comprehensive overview of the current state-of-the-art in NLP and ML for combating fake news and hate speech, and to inspire further research in this important area.
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