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Overview of the Benefits Deep Learning Can Provide Against Fake News, Cyberbullying and Hate Speech

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Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 721))

Abstract

Deep learning is a feasible technology and is the best replacement for traditional means to prevent fake news, cyberbullying and hate speech. Traditional methods to prevent fake news, cyberbullying, and hate speech include using real life personnel to go through messages and remove them. The research analyses other researchers’ discoveries relative to deep learning. It is important to conduct this research so that we are all aware of how close we are to be able to protect our future generations of children and adults from having their mental and physical health affected. This research aims to analyse the current deep learning techniques used to prevent fake news, cyberbullying and hate speech. A comparison is included in this research to identify the state-of-the-art technique.

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Authors and Affiliations

  1. Western Sydney University, Sydney, Australia

    Thair Al-Dala’in & Justin Hui San Zhao

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  1. Thair Al-Dala’in
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  2. Justin Hui San Zhao
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Correspondence to Justin Hui San Zhao .

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Editors and Affiliations

  1. University of Detroit Mercy, Farmington Hills, MI, USA

    Kevin Daimi

  2. Asia Pacific International College, Sydney, NSW, Australia

    Abeer Al Sadoon

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Al-Dala’in, T., Zhao, J.H.S. (2023). Overview of the Benefits Deep Learning Can Provide Against Fake News, Cyberbullying and Hate Speech. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23). Lecture Notes in Networks and Systems, vol 721. Springer, Cham. https://doi.org/10.1007/978-3-031-35308-6_2

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