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Detecting Hateful Content on Social Media: A Survey

Aaniya Gouse, Afaq Alam Khan

Abstract


Spreading hatred over social media is a growing menace. Whether in the form of discrimination, cyberbullying or trolls, malicious posts have pestered users throughout the globe. To overcome this nuisance, an automated hate text detector has almost become a necessity. Despite a few inherent limitations that hate speech detection has, there is work going on in this field,andthe scope for progress remains. Besides various techniques used in the state-of-the-art, this paper extends a précis over outcomes of the latest experiments in the field. The most recent and updated approaches used in the field along with the limitations, positives of each approach and latest techniques used in the field of hate speech detection are discussed in this paper.

Keywords: Hate Speech, Natural Language Processing, Neural Networks, Supervised Learning, Social Media

Cite this Article: AaniyaGouse, AfaqAlam Khan.Detecting Hateful Content on Social Media: A Survey. Journal of Artificial Intelligence Research & Advances. 2020; 7(3): 1–9p.


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DOI: http://dx.doi.org/10.37591%2Fjoaira.v7i3.2509

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