Text and Social Media Analytics for Fake News Hate Speech Detection

Identifying and stopping the dissemination of fabricated news, hate speech, or deceptive information camouflaged as legitimate news poses a significant technological hurdle. This book presents emergent methodologies and technological approaches of natural language processing through machine learning for counteracting such instances. Identifying and stopping the dissemination of fabricated news, hate speech, or deceptive information camouflaged as legitimate news poses a significant technological hurdle. This book presents emergent methodologies and technological approaches of natural language processing through machine learning for counteracting the spread of fake news and hate speech on social media platforms. Covers various approaches, algorithms, and methodologies for fake news and hate speech detection Explains the automatic detection and prevention of fake news and hate speech through paralinguistic clues on social media using artificial intelligence Discusses the application of machine learning models to learn linguistic characteristics of hate speech over social media platforms Emphasizes the role of multilingual and multimodal processing to detect fake news Includes research on different optimization techniques, case studies on the identification, prevention, and social impact of fake news, and GitHub repository links to aid understanding The text is for professionals and scholars of various disciplines interested in fake news and hate speech detection.

Pages: 306, Edition: 1, Paperback, Chapman and Hall/CRC