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A Framework for Detection of Hate Speech in Social Media: Achieving Societal Resilience
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Author(s): Nitish Kumar Ojha (Amity University, Noida, India), Abhishek Vaish (Indian Institute of Information Technology, Allahabad, India), Shreyansh Dwivedi (Indian Institute of Information Technology, Allahabad, India), Brijesh Kumar Markandey (Indian Institute of Information Technology, Allahabad, India)and Rania El-Gazzar (University of South-Eastern Norway, Norway)
Copyright: 2025
Pages: 22
Source title:
Ethical AI Solutions for Addressing Social Media Influence and Hate Speech
Source Author(s)/Editor(s): Swati Chakraborty (Concordia University, Canada)
DOI: 10.4018/979-8-3693-9904-0.ch010
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Abstract
The authenticity and the orientation of information in social media require designing a complex information system to analyze the artifacts that may cause a positive or negative impact on the peace order of the global societal structure. Social cyber security is a new paradigm that is gaining momentum worldwide; every country is investing in capacity-building measures, R&D, and strategies to combat the menace of the problem caused by the abundance of manipulative information. Societal cyber security has become a multi-million-dollar market because economic change has monetized data, and it is a top priority for every government to ensure that the digital society is secure and safe. Therefore, it is pertinent to find a framework that can help detect information that can negatively impact society at large. In this paper, we are using machine learning techniques for classification in a fusion dataset of manipulative information that has been concatenated with the benchmark dataset. The subclasses are real news without hate speech, real news with hate speech, fake news with hate speech, and fake news without hate speech. The application of this framework is to be used to mitigate the threat to society through the widespread use of manipulative information and help society to be resilient against such threats.
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