Text And Social Media Analytics For Fake News And 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.

1. Analysis of fake news detection and prevention of various fake news detection algorithms and its research challenges 2. Detection and Prevention of Fake News and Hate Speech through Artificial Intelligence Learning Techniques and Natural Language Processing 3. Detection of Fake News and Hate Crimes on Elections using Machine learning Approaches 4. Analytics of text and social media for Challenges of Hateful & Offensive Speech Detection 5. The Ripple Effect of Fake News and Hate Speech on Elections and its countermeasures using Machine Learning Methodologies: A Critical Analysis 6. Explainable Models for the Detection of Incidents of Fake News and Hate Speech 7. Gender biases and politics: The semiotics of hate speech on the internet 8. Social Media Platforms and COVID-19 Related Misinformation in India: Acceptance, Spread, and Implications 9. SIR Model for Understanding the Spread of Fake News and Hate Speech 10. Impact on Fake News in Social Media and Current Technology in Detection of Fake News in Social Media 11. The role of the connotative meaning of Emotive Expressions in Shaping Political Conflicts. An Analytical study of Political Emotive Expressions in Arab Spring Countries 12. Detecting Fake News and Hate Speech Using Machine Learning: An Overview of Frameworks 13. Hate content identification in code-mixed social media data 14. Social Media Skirmish: Dealing with fake news, misleading information and propaganda using AI and ML 15. Detection and prevention of fake news and hate speech through machine learning and natural language processing 16. Fake News and Hate Speech: Influence on society and Detection by Machine Language

Dr. Hemant Kumar Soni has done M. Tech (IT) from Bundelkhand University, Jhansi, Uttar Pradesh, India and Doctoral degree in Computer Science and Engineering from Amity University Madhya Pradesh, Gwalior, India. He has 26 years of teaching experience for UG and PG courses in Computer Science and presently working as Associate Professor in the Department of Computer Science and Engineering at Amity University, Madhya Pradesh. Currently 6 scholars are pursuing their PhD under his guidance. He also bears the responsibility of Univers