Classification cyber harassment on Twitter using multinomial naïve bayes

Oktavia, Kiki, Karisma, Ria Dhea Layla Nur ORCID: https://orcid.org/0000-0002-5941-9565 and Widayani, Heni ORCID: https://orcid.org/0000-0002-6966-6754 (2025) Classification cyber harassment on Twitter using multinomial naïve bayes. Matics: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology), 17 (2). pp. 52-62. ISSN 2477-2550

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Abstract

The study uses the Multinomial Naïve Bayes (MNB) method to classify four types of cyber harassment on Twitter, namely Physical Threats, Purposeful Embarrassment, Racist, and Sexual Harassment. A total of 2,000 Indonesian language tweets were used as samples and have been manually labeled using training data and testing data. The classification results show that the model achieves an accuracy of 77%, with a consistent accuracy value of 76.21% based on the K-fold cross-validation test. This study shows that MNB is effective in multiclass text classification to detect cyber harassment and provides a computationally efficient solution to the real-time content moderation system.

Item Type:	Journal Article
Keywords:	Cyber Harassment; Multinomial Naïve Bayes; Twitter; Purposeful Embarrassment; Physical Threats; Racist; Sexual
Subjects:	01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010404 Probability Theory
Divisions:	Faculty of Mathematics and Sciences > Department of Mathematics
Depositing User:	Miss Ria Dhea Layla Nur Karisma
Date Deposited:	06 Oct 2025 08:15

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