Offensive Language and Hate Speech Detection using BERT Model

Amalia, Fadila Shely; Suyanto, Yohanes

doi:10.22146/ijccs.99841

Open access Jurnal SINTA 2

Offensive Language and Hate Speech Detection using BERT Model

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 18 No. 4 (2024) · pp. 371–380

DOI: 10.22146/ijccs.99841
ISSN: 2460-7258, 1978-1520
Published: 2024-10-31
Publisher: IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language: eng
License: creativecommons.org/licenses/by-sa/4.0

Abstract

Hate speech detection is an important issue in sentiment analysis and natural language processing. This study aims to improve the effectiveness of hate speech detection in English text using the BERT model, along with modified preprocessing techniques to enhance the F1-score. The dataset, sourced from Kaggle, contains English text with hate speech content. Evaluation results show a significant improvement in the model's accuracy and overall text classification performance. The BERT model achieved 89.11% accuracy on test data, correctly predicting 85 out of 95 samples. While the model excels at classifying offensive text with around 95% accuracy, it struggles to distinguish between hate and offensive text, with some confusion between neither and offensive categories. The classification report shows F1-scores of 0.43 for the hate class, 0.94 for the offensive class, and 0.84 for the neither class, with a weighted average F1-score of 0.89 and a macro average of 0.73. These results indicate that the BERT model delivers solid performance in detecting hate speech, though there is room for improvement, particularly in distinguishing certain classes.

Keywords

Collaboration network of the authors

10 authors · 14 co-authorship links · 2 across different organisations

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Universitas Gadjah Mada
Universitas Teknokrat Indonesia

Different organisations
Same organisation
Affiliation unknown
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Co-author	Organisation	Joint articles
Agus Sihabuddin	Universitas Gadjah Mada	2
Yunita Sari	Universitas Gadjah Mada	2
Sigit Priyanta	Universitas Gadjah Mada	1
Mhd. Theo Ari Bangsa	Universitas Gadjah Mada	1
Auliya Rahman Isnain	Universitas Teknokrat Indonesia	1
Annisaa Utami	Universitas Gadjah Mada	1
Riszki Wijayatun Pratiwi	Universitas Gadjah Mada	1
Sensa Gudya Sauma Syahra	Universitas Gadjah Mada	1

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">Offensive Language and Hate Speech Detection using BERT Model</dc:title>
	<dc:creator>Amalia, Fadila Shely</dc:creator>
	<dc:creator>Suyanto, Yohanes</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science - NLP</dc:subject>
	<dc:subject xml:lang="en-US">Hate speech; Offensive; Deep Learning; BERT; Twitter</dc:subject>
	<dc:description xml:lang="en-US">Hate speech detection is an important issue in sentiment analysis and natural language processing. This study aims to improve the effectiveness of hate speech detection in English text using the BERT model, along with modified preprocessing techniques to enhance the F1-score. The dataset, sourced from Kaggle, contains English text with hate speech content. Evaluation results show a significant improvement in the model's accuracy and overall text classification performance. The BERT model achieved 89.11% accuracy on test data, correctly predicting 85 out of 95 samples. While the model excels at classifying offensive text with around 95% accuracy, it struggles to distinguish between hate and offensive text, with some confusion between neither and offensive categories. The classification report shows F1-scores of 0.43 for the hate class, 0.94 for the offensive class, and 0.84 for the neither class, with a weighted average F1-score of 0.89 and a macro average of 0.73. These results indicate that the BERT model delivers solid performance in detecting hate speech, though there is room for improvement, particularly in distinguishing certain classes.</dc:description>
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	<dc:date>2024-10-31</dc:date>
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	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 18, No 4 (2024): October; 371-380</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/99841/40365</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
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