A Systematic Literature Review of Different Machine Learning Methods on Hate Speech Detection

Calvin Erico Rudy Salim - Computer Science Department, BINUS Graduate Program, Master of Computer Science, Bina Nusantara University, Jakarta, Indonesia
Derwin Suhartono - Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia


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DOI: http://dx.doi.org/10.30630/joiv.4.4.476

Abstract


Hate speech is one of the most challenging problem internet is facing today. This systematic literature review examine hate speech detection problem and will be used to do an experimental approach on detecting hate speech and abusive language. This work also provide an overview of previous research, including methods, algorithms, and main features used. We use two research questions in this literature review which will be the foundation of the next experimental research. Correctly classifying a piece of text as an actual hate speech requires a lot of correctly labelled data. Most common challenges are different languages, out of vocabulary words, long range dependencies and many more. 

Keywords


natural language processing; artificial intelligence; hate-speech

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JOIV : International Journal on Informatics Visualization
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Creative Commons License is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.