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An Explainable AI Model for Hate Speech Detection on Indonesian Twitter
CommIT (Communication and Information Technology) Journal
ISSN 2460-7010, 1979-2484
Terbit 2022-06-08
Penerbit Bina Nusantara University
Bahasa eng
Abstrak
To avoid citizen disputes, hate speech on social media, such as Twitter, must be automatically detected. The current research in Indonesian Twitter focuses on developing better hate speech detection models. However, there is limited study on the explainability aspects of hate speech detection. The research aims to explain issues that previous researchers have not detailed and attempt to answer the shortcomings of previous researchers. There are 13,169 tweets in the dataset with labels like “hate speech†and “abusive languageâ€. The dataset also provides binary labels on whether hate speech is directed to individual, group, religion, race, physical disability, and gender. In the research, classification is performed by using traditional machine learning models, and the predictions are evaluated using an Explainable AI model, such as Local Interpretable Model-Agnostic Explanations (LIME), to allow users to comprehend why a tweet is regarded as a hateful message. Moreover, models that perform well in classification perceive incorrect words as contributing to hate speech. As a result, such models are unsuitable for deployment in the real world. In the investigation, the combination of XGBoost and logical LIME explanations produces the most logical results. The use of the Explainable AI model highlights the importance of choosing the ideal model while maintaining users’ trust in the deployed model.
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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.binus.ac.id/index.php/commit/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
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<dc:title xml:lang="en-US">An Explainable AI Model for Hate Speech Detection on Indonesian Twitter</dc:title>
<dc:creator>Ibrahim, Muhammad Amien</dc:creator>
<dc:creator>Arifin, Samsul</dc:creator>
<dc:creator>Yudistira, I Gusti Agung Anom</dc:creator>
<dc:creator>Nariswari, Rinda</dc:creator>
<dc:creator>Abdillah, Abdul Azis</dc:creator>
<dc:creator>Murnaka, Nerru Pranuta</dc:creator>
<dc:creator>Prasetyo, Puguh Wahyu</dc:creator>
<dc:subject xml:lang="en-US">Artificial Intelligence Model</dc:subject>
<dc:subject xml:lang="en-US">Hate Speech</dc:subject>
<dc:subject xml:lang="en-US">Indonesian Twitter</dc:subject>
<dc:description xml:lang="en-US">To avoid citizen disputes, hate speech on social media, such as Twitter, must be automatically detected. The current research in Indonesian Twitter focuses on developing better hate speech detection models. However, there is limited study on the explainability aspects of hate speech detection. The research aims to explain issues that previous researchers have not detailed and attempt to answer the shortcomings of previous researchers. There are 13,169 tweets in the dataset with labels like “hate speech†and “abusive languageâ€. The dataset also provides binary labels on whether hate speech is directed to individual, group, religion, race, physical disability, and gender. In the research, classification is performed by using traditional machine learning models, and the predictions are evaluated using an Explainable AI model, such as Local Interpretable Model-Agnostic Explanations (LIME), to allow users to comprehend why a tweet is regarded as a hateful message. Moreover, models that perform well in classification perceive incorrect words as contributing to hate speech. As a result, such models are unsuitable for deployment in the real world. In the investigation, the combination of XGBoost and logical LIME explanations produces the most logical results. The use of the Explainable AI model highlights the importance of choosing the ideal model while maintaining users’ trust in the deployed model.</dc:description>
<dc:publisher xml:lang="en-US">Bina Nusantara University</dc:publisher>
<dc:date>2022-06-08</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
<dc:format>application/pdf</dc:format>
<dc:identifier>https://journal.binus.ac.id/index.php/commit/article/view/8343</dc:identifier>
<dc:identifier>10.21512/commit.v16i2.8343</dc:identifier>
<dc:source xml:lang="en-US">CommIT (Communication and Information Technology) Journal; Vol. 16 No. 2 (2022): CommIT Journal ; 175-182</dc:source>
<dc:source>2460-7010</dc:source>
<dc:source>1979-2484</dc:source>
<dc:language>eng</dc:language>
<dc:relation>https://journal.binus.ac.id/index.php/commit/article/view/8343/4574</dc:relation>
<dc:rights xml:lang="en-US">Copyright (c) 2022 Samsul Arifin</dc:rights>
<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>
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APA 7
Ibrahim, M. A., Arifin, S., Yudistira, I. G. A. A., Nariswari, R., Abdillah, A. A., Murnaka, N. P., & Prasetyo, P. W. (2022). An Explainable AI Model for Hate Speech Detection on Indonesian Twitter. CommIT (Communication and Information Technology) Journal, 16(2), 175–182. https://doi.org/10.21512/commit.v16i2.8343
Salin
IEEE
M. A. Ibrahim, S. Arifin, I. G. A. A. Yudistira, R. Nariswari, A. A. Abdillah, N. P. Murnaka, and P. W. Prasetyo, “An Explainable AI Model for Hate Speech Detection on Indonesian Twitter,” CommIT (Communication and Information Technology) Journal, vol. 16, no. 2, pp. 175–182, 2022, doi: 10.21512/commit.v16i2.8343.
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