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Hate Speech Detection in Indonesian Twitter using Contextual Embedding Approach

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 15 No. 2 (2021) · pp. 177–188

DOI
10.22146/ijccs.64916
ISSN
2460-7258, 1978-1520
Published
2021-04-30
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Hate speech develops along with the rapid development of social media. Hate speech is often issued due to a lack of public awareness of the difference between criticism and statements that might contribute to this crime. Therefore, it is very important to do early detection of sentences that will be written before causing a criminal act due to public ignorance. In this paper, we use the advancement of deep neural networks to predict whether a sentence contains a hate speech and abusive tone. We demonstrate the robustness of different word and contextual embedding to represent the semantic of hate speech words. In addition, we use a document embedding representation via a recurrent neural networks with gated recurrent unit as the main architecture to provide richer representation. Compared to syntactic representation of the previous approach, the contextual embedding in our model proved to give a significant boost on the performance by a significant margin.

Keywords

Collaboration network of the authors

13 authors · 23 co-authorship links · 6 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Gadjah Mada
  • Udayana University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Afiahayati AfiahayatiUniversitas Gadjah Mada1
Muhammad AuzanUniversitas Gadjah Mada1
Faiz Unisa JazadiUniversitas Gadjah Mada1
Wayan Oger VihikanUdayana University1
I Made Wiraharja Jaya PutraUdayana University1
Marko Ferdian SalimUniversitas Gadjah Mada1
M. SyairajiUniversitas Gadjah Mada1
Dian Budi SantosoUniversitas Gadjah Mada1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Hate Speech Detection in Indonesian Twitter using Contextual Embedding Approach</dc:title>
	<dc:creator>Herwanto, Guntur Budi</dc:creator>
	<dc:creator>Ningtyas, Annisa Maulida</dc:creator>
	<dc:creator>Mujiyatna, I Gede</dc:creator>
	<dc:creator>Nugraha, Kurniawan Eka</dc:creator>
	<dc:creator>Prayana Trisna, I Nyoman</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">hate speech; natural language processing; deep neural network; contextual embedding; recurrent neural network</dc:subject>
	<dc:description xml:lang="en-US">Hate speech develops along with the rapid development of social media. Hate speech is often issued due to a lack of public awareness of the difference between criticism and statements that might contribute to this crime. Therefore, it is very important to do early detection of sentences that will be written before causing a criminal act due to public ignorance. In this paper, we use the advancement of deep neural networks to predict whether a sentence contains a hate speech and abusive tone. We demonstrate the robustness of different word and contextual embedding to represent the semantic of hate speech words. In addition, we use a document embedding representation via a recurrent neural networks with gated recurrent unit as the main architecture to provide richer representation. Compared to syntactic representation of the previous approach, the contextual embedding in our model proved to give a significant boost on the performance by a significant margin.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2021-04-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/64916</dc:identifier>
	<dc:identifier>10.22146/ijccs.64916</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 15, No 2 (2021): April; 177-188</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/64916/31157</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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