Enhancing hate speech detection in Indonesian using abusive words lexicon

Endang Wahyu Pamungkas, Dian Purworini, Divi Galih Prasetyo Putri, Sohail Akhtar


Hate speech is a major challenge in Indonesia, a diverse country with multiple languages and a dynamic online landscape. This research explores the phenomenon of hate speech and its detection, particularly in language contexts with limited resources. We introduce a new abusive words lexicon, created by collecting words from various sources, adapted for Indonesian, Javanese and Sundanese. Our study investigates the practical implementation of this lexicon. We conducted extensive experiments using different datasets and machine learning models, aiming to improve hate speech detection. The results consistently show a positive impact of the lexicon, which significantly improves detection, especially in languages with fewer resources. But this research paves the way for further exploration. The lexicon can be expanded, broadening its scope. Additionally, we suggest investigating more sophisticated models, such as transformerbased models, to more effectively detect hate speech. In a world where hate speech is a growing problem, our research provides valuable insights and tools to combat it effectively in Indonesia and other countries.


Abusive language; Hate speech detection; Low-resource languages; Machine learning; Social media

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DOI: http://doi.org/10.11591/ijeecs.v33.i1.pp450-462


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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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