Analytics of Text and Social Media for Challenges of Hateful and Offensive Speech Detection
Anand Kumar Mishra, C. S. Raghuvanshi, Hemant Kumar Soni, Pragya Goswami · 2024
The increasing popularity of social media platforms has resulted in a rapid increase in the spread of fake news and hate speech, which can have negative impacts on society. Identifying and preventing the spread of such content has become an important and crucial task for researchers and practitioners. This chapter aims to present an overview of text and social media analytics techniques for detecting fake and unreal news and hate speech. Section 4.1 of the chapter consists of the basics of fake news and hate speech, with definitions and examples. Section 4.2 includes text and social media analytics techniques, including natural language processing, sentiment analysis, and network analysis. Section 4.3 provides case studies of fake news and hate speech detection, focusing on the effectiveness of various approaches. Section 4.4 discusses the difficulties of detecting fake news and hate speech, the absence of a standard definition, and the constantly evolving nature of the problem. Section 4.5 explores future research fields in this area, including the use of machine learning techniques and the involvement of multiple modalities. In a nutshell, this chapter gives a comprehensive overview of the state-of-the-art in text and social media analytics for fake news and hate speech detection. It will be of interest to researchers, practitioners, and policymakers concerned with the spread of harmful content on social media platforms.