An Explainable AI Model for Hate Speech Detection on Indonesian Twitter
DOI:
https://doi.org/10.21512/commit.v16i2.8343Keywords:
Artificial Intelligence Model, Hate Speech, Indonesian TwitterAbstract
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 language”. 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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References
Z. Zhang and L. Luo, “Hate speech detection: A solved problem? The challenging case of long tail on Twitter,” Semantic Web, vol. 10, no. 5, pp. 925–945, 2019.
G. H. Stanton, “The Rwandan genocide: Why early warning failed,” Journal of African Conflicts and Peace Studies, vol. 1, no. 2, pp. 6–25, 2009.
A. A. Abdillah, A. Azwardi, S. Permana, I. Susanto, F. Zainuri, and S. Arifin, “Performance evaluation of linear discriminant analysis and support vector machines to classify cesarean section,” Eastern-European Journal of Enterprise Technologies, vol. 5, no. 2, pp. 37–43, 2021.
T. Davidson, D. Warmsley, M. Macy, and I. Weber, “Automated hate speech detection and the problem of offensive language,” in Proceedings of the International AAAI Conference on Web and Social Media, vol. 11, no. 1, 2017, pp. 512–515.
M. O. Ibrohim and I. Budi, “Multi-label hate speech and abusive language detection in Indonesian Twitter,” in Proceedings of the Third Workshop on Abusive Language Online, 2019, pp. 46–57.
H. Yenala, A. Jhanwar, M. K. Chinnakotla, and J. Goyal, “Deep learning for detecting inappropriate content in text,” International Journal of Data Science and Analytics, vol. 6, no. 4, pp. 273–286, 2018.
S. Arifin, I. B. Muktyas, W. F. Al Maki, and M. K. B. Mohd Aziz, “Graph coloring program of exam scheduling modeling based on Bitwise coloring algorithm using Python,” Journal of Computer Science, vol. 18, no. 1, pp. 26–32, 2022.
S. Frenda, B. Ghanem, M. Montes-Y-G´omez, and P. Rosso, “Online hate speech against women: Automatic identification of misogyny and sexism on Twitter,” Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4743–4752, 2019.
I. Alfina, R. Mulia, M. I. Fanany, and Y. Ekanata, “Hate speech detection in the Indonesian language: A dataset and preliminary study,” in 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS). Bali, Indonesia: IEEE, Oct. 28–29, 2017, pp. 233–238.
F. Del Vigna, A. Cimino, F. Dell’Orletta, M. Petrocchi, and M. Tesconi, “Hate me, hate me not: Hate speech detection on Facebook,” in Proceedings of the First Italian Conference on Cybersecurity (ITASEC17), 2017, pp. 86–95.
C. Nobata, J. Tetreault, A. Thomas, Y. Mehdad, and Y. Chang, “Abusive language detection in online user content,” in Proceedings of the 25th International Conference on World Wide Web, 2016, pp. 145–153.
M. O. Ibrohim, E. Sazany, and I. Budi, “Identify abusive and offensive language in Indonesian Twitter using deep learning approach,” Journal of Physics: Conference Series, vol. 1196, no. 1, pp. 1–6, 2019.
S. Tuarob and J. L. Mitrpanont, “Automatic discovery of abusive Thai language usages in social networks,” in International Conference on Asian Digital Libraries. Bangkok, Thailand: Springer, Nov. 13–15, 2017, pp. 267–278.
S. Arifin, I. B. Muktyas, P. W. Prasetyo, and A. A. Abdillah, “Unimodular matrix and Bernoulli map on text encryption algorithm using Python,” Al-Jabar: Jurnal Pendidikan Matematika, vol. 12, no. 2, pp. 447–455, 2021.
P. El Kafrawy, A. Mausad, and H. Esmail, “Experimental comparison of methods for multi-label classification in different application domains,” International Journal of Computer Applications, vol. 114, no. 19, pp. 1–9, 2015.
A. B. Arrieta, N. D´ıaz-Rodr´ıguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. Garc´ıa, S. Gil-L´opez, D. Molina, R. Benjamins et al., “Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI,” Information Fusion, vol. 58, pp. 82–115, 2020.
A. Mahajan, D. Shah, and G. Jafar, “Explainable AI approach towards toxic comment classification,” in Emerging technologies in data mining and information security. Springer, 2021, pp. 849–858.
G. I. P´erez-Landa, O. Loyola-Gonz´alez, and M. A. Medina-P´erez, “An explainable artificial intelligence model for detecting xenophobic tweets,” Applied Sciences, vol. 11, no. 22, pp. 1–27, 2021.
Ł. G´orski and S. Ramakrishna, “Explainable artificial intelligence, lawyer’s perspective,” in Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law, 2021, pp. 60–68.
I. B. Muktyas, Sulistiawati, and S. Arifin, “Digital image encryption algorithm through unimodular matrix and logistic map using Python,” in AIP Conference Proceedings, vol. 2331, no. 1. AIP Publishing LLC, 2021, pp. 020 006–1–020 006–7.
M. T. Ribeiro, S. Singh, and C. Guestrin, ““why should I trust you?” Explaining the predictions of any classifier,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 1135–1144.
F. Z. Tala, “A study of stemming effects on information retrieval in Bahasa Indonesia,” Master thesis, Universiteit van Amsterdam, 2003.
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