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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.
News Framing Through English-Chinese Translation provides a useful tool to depict how Chinese news translation can be examined in the era of globalization. The author has integrated framing theory in journalism studies with translation studies and developed a new theoretical model/framework named Transframing. This interdisciplinary model is pioneering and will make theoretical and conceptual contributions to translation studies. This book aims to reveal ideological, sociocultural and linguistic factors creating media discourse by examining Chinese media discourse, in comparison to its counterpart in English. Through the analysis of both quantitative and qualitative methods, it is concluded that the transframing model can be applied to interpreting, describing, explaining as well as predicting the practice of news translation.