Misinformation Containment Using NLP and Machine Learning: Why the Problem Is Still Unsolved

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Author(s): Vishnu S. Pendyala (San Jose State University, USA)
Copyright: 2023
Pages: 16
Source title: Deep Learning Research Applications for Natural Language Processing
Source Author(s)/Editor(s): L. Ashok Kumar (PSG College of Technology, India), Dhanaraj Karthika Renuka (PSG College of Technology, India)and S. Geetha (Vellore Institute of Technology, India)
DOI: 10.4018/978-1-6684-6001-6.ch003

Keywords: Artificial Intelligence / Engineering Science Reference / Knowledge Management and Library Science / Natural Language Processing

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Abstract

Despite the increased attention and substantial research into it claiming outstanding successes, the problem of misinformation containment has only been growing in the recent years with not many signs of respite. Misinformation is rapidly changing its latent characteristics and spreading vigorously in a multi-modal fashion, sometimes in a more damaging manner than viruses and other malicious programs on the internet. This chapter examines the existing research in natural language processing and machine learning to stop the spread of misinformation, analyzes why the research has not been practical enough to be incorporated into social media platforms, and provides future research directions. The state-of-the-art feature engineering, approaches, and algorithms used for the problem are expounded in the process.

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Misinformation Containment Using NLP and Machine Learning: Why the Problem Is Still Unsolved

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