This book informs readers on how to understand, detect, and mitigate hate speech in online social media. The authors first cover the definition of hate speech and how its prevalence can be measured on online social media platforms using text and graph-based methods. The book then describes the process of detecting hate speech and presents a comprehensive account of the AI models that are currently being used. Further, the authors discuss the associated challenges that must be overcome while using these models. The book concludes with an overview of the mitigation techniques for hate speech, including blocking or suspension of the accounts (hard technique) and counterspeech (soft technique), and a discussion of the effects of these techniques on social media platforms.

Analyzes the language used in hate speech, the users who post the content, and the target groups affected by it Explores the methods that detect hate speech automatically, from machine learning models to deep learning-based models Proposes a mitigation pipeline utilizing a combination of several methods for combating hate speech

Autorentext

Punyajoy Saha holds a Ph.D. in Computer Science and Engineering from IIT Kharagpur, West Bengal. His research interests lie in the intersection of computational social science and natural language processing.

Mithun Das holds a Ph.D. in Computer Science and Engineering from the Indian Institute of Technology, Kharagpur, with research interests in computational social science and natural language processing.

Animesh Mukherjee, Ph.D., is a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology, Kharagpur.

Inhalt
Introduction.- Analysis.- Detection.- Mitigation.- Challenges and the Road Ahead. <p