Deep neural network approaches in hate speech detection

Chengxuan Du; Xin Weng

doi:10.1117/12.2628169

22 April 2022 Deep neural network approaches in hate speech detection

Chengxuan Du, Xin Weng

Proceedings Volume 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021); 121632O (2022) https://doi.org/10.1117/12.2628169
Event: International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2021, Nanjing, China

Abstract

Over the past decade, the number of automated communication and posts on social media platforms has made it much easier to generate and spread hate speech in tandem with the related social implications. Social media companies have experienced intense pressure to address the issue and help minimize incidences of hate speech on their platforms. In this regard, machine language processing techniques such as natural language processing can help detect online hate speech. Natural language processing is a branch of machine language that enables one to understand human speech, analyze, manipulate it, and potentially understand language generated by humans. Other deep learning techniques that could help explore this subject and improve hate speech detection, such as convolutional neural network, recurrent neural network, and graph neural network, will be explored in this paper.

Citation Download Citation

Chengxuan Du and Xin Weng "Deep neural network approaches in hate speech detection", Proc. SPIE 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 121632O (22 April 2022); https://doi.org/10.1117/12.2628169

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