Social media has transformed the way information is shared, making it a powerful tool for communication, influence, and engagement. However, this has also led to significant challenges, such as the spread of fake news, misinformation, and hate speech, which can have severe social, political, and economic consequences. Detecting and mitigating these issues is crucial to ensuring the integrity of online discourse, protecting individuals from harm, and maintaining trust in digital platforms.

This book explores the intersection of social media analytics, artificial intelligence, natural language processing (NLP) and cybersecurity to address these pressing challenges. The authors provide a comprehensive and research-driven exploration of techniques and applications for detecting online misinformation and toxicity, fake news and hate speech in social media. Coverage includes the role of social networks, bots, and coordinated disinformation campaigns, machine learning and deep learning approaches, large language models (LLMs) in the identification of fake and hate speech identification, NLP methods, fact-checking systems, and multimodal content analysis. Hands-on implementation guides, real-world case studies and ethical considerations offer practical insights into both techniques and applications.

Bridging the gap between academic research and practical applications, the book is intended for researchers, students, lecturers, data scientists, cybersecurity professionals, policymakers, working in AI-driven content moderation and social media analytics.