Tren Riset Deteksi Ujaran Kebencian: Analisis Bibliometrik 2020–2025
DOI:
https://doi.org/10.70609/jusifor.v5i1.8769Keywords:
Hate Speech, Natural Language Processing, Transformer Model, Bibliometric Analysis, Multimodal and Multilingual ApproacesAbstract
A The rapid growth of social media has increased the risk of hate speech proliferation, driving extensive research in Natural Language Processing (NLP) to develop more accurate automatic detection systems. Over the past decade, hate speech detection approaches have evolved significantly, shifting from traditional machine learning methods to deep learning architectures and advanced transformer-based models. However, comprehensive bibliometric studies that map methodological developments and implementation domains remain limited. This research analyzes 1,335 publications indexed in Scopus to identify trends in methodological approaches (e.g., SVM, Naive Bayes, LSTM, and BERT-family models) and application domains (Twitter, Facebook, YouTube, and multilingual contexts). Keyword extraction and temporal trend visualization were conducted using Python. The findings indicate that transformer models have dominated research since 2020, accompanied by a shift from single-text analysis toward multimodal and multilingual approaches. This study highlights future research directions involving transformer integration, multilingual processing, and Explainable AI (XAI) to enhance transparency in hate speech detection.
References
[1] M. S. Jahan and M. Oussalah, “A systematic review of hate speech automatic detection using natural language processing,” Neurocomputing, vol. 546, Art. no. 126232, Aug. 2023, doi: 10.1016/j.neucom.2023.126232.
[2] S. Mukherjee and S. Das, “Application of Transformer-based language models to detect hate speech in social media,” J. Comput. Cogn. Eng., vol. 2, no. 4, pp. 278–286, Dec. 2021, doi: 10.47852/bonviewJCCE2022010102.
[3] P. Fortuna and S. Nunes, “A survey on automatic detection of hate speech in text,” ACM Comput Surv, vol. 51, no. 4, pp. 1–30, Jul. 2018, doi: 10.1145/3232676.
[4] J. S. Malik, G. Pang, and A. Van Den Hengel, “Deep learning for hate speech detection: A comparative study,” arXiv (Cornell University), Feb. 2022, doi: 10.48550/arxiv.2202.09517.
[5] G. Ramos et al., “A comprehensive review on automatic hate speech detection in the age of the transformer,” Soc. Netw. Anal. Min., vol. 14, no. 1, Art. no. 204, Oct. 2024, doi: 10.1007/s13278-024-01361-3.
[6] G. K. Shahi, A. Dirkson, and T. A. Majchrzak, “An exploratory study of COVID-19 misinformation on Twitter,” Online Soc. Netw. Media, vol. 22, Art. no. 100104, Mar. 2021, doi: 10.1016/j.osnem.2020.100104.
[7] H. Saleh, A. Alhothali, and K. Moria, “Detection of hate speech using BERT and hate speech word embedding with deep model,” arXiv (Cornell University), Nov. 2021, doi: 10.48550/ARXIV.2111.01515.
[8] V. Basile et al., “SemEval-2019 Task 5: Multilingual detection of hate speech against immigrants and women in twitter,” in Proceedings of the 13th International Workshop on Semantic Evaluation, Minneapolis, Minnesota, USA: Association for Computational Linguistics, 2019, pp. 54–63. doi: 10.18653/v1/S19-2007.
[9] G. K. Shahi and T. A. Majchrzak, “Hate speech detection using cross-platform social media data in english and german language,” arXiv (Cornell University), 2024, doi: 10.48550/ARXIV.2410.05287.
[10] H. Wang, T. R. Yang, U. Naseem, and R. K.-W. Lee, “MultiHateClip: A multilingual benchmark dataset for hateful video detection on YouTube and Bilibili,” arXiv (Cornell University), Jul. 2024, doi: 10.48550/ARXIV.2408.03468.
[11] A. Chhabra and D. K. Vishwakarma, “A literature survey on multimodal and multilingual automatic hate speech identification,” Multimed. Syst., vol. 29, no. 3, pp. 1203–1230, Jun. 2023, doi: 10.1007/s00530-023-01051-8.
[12] N. Donthu, S. Kumar, D. Mukherjee, N. Pandey, and W. M. Lim, “How to conduct a bibliometric analysis: An overview and guidelines,” J. Bus. Res., vol. 133, pp. 285–296, Sep. 2021, doi: 10.1016/j.jbusres.2021.04.070.
[13] S. Mariappanadar, “Do HRM systems impose restrictions on employee quality of life? Evidence from a sustainable HRM perspective,” J. Bus. Res., vol. 118, pp. 38–48, Sep. 2020, doi: 10.1016/j.jbusres.2020.06.039.
[14] F. Poletto, V. Basile, M. Sanguinetti, C. Bosco, and V. Patti, “Resources and benchmark corpora for hate speech detection: a systematic review,” Lang. Resour. Eval., vol. 55, no. 2, pp. 477–523, Jun. 2021, doi: 10.1007/s10579-020-09502-8.
[15] E. Chandrasekharan, S. Jhaver, A. Bruckman, and E. Gilbert, “Quarantined! Examining the effects of a community-wide moderation intervention on Reddit,” arXiv (Cornell University), Sep. 2020, doi: 10.48550/arXiv.2009.11483.
[16] J. Baas, M. Schotten, A. Plume, G. Côté, and R. Karimi, “Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies,” Quant. Sci. Stud., vol. 1, no. 1, pp. 377–386, Feb. 2020, doi: 10.1162/qss_a_00019.
[17] J. A. Moral-Muñoz, E. Herrera-Viedma, A. Santisteban-Espejo, and M. J. Cobo, “Software tools for conducting bibliometric analysis in science: An up-to-date review,” El Prof. Inf., vol. 29, no. 1, Art. no. e290103, Jan. 2020, doi: 10.3145/epi.2020.ene.03.
[18] W. M. Lim, S. Kumar, and N. Donthu, “How to combine and clean bibliometric data and use bibliometric tools synergistically: Guidelines using metaverse research,” J. Bus. Res., vol. 182, Art. no. 114760, Sep. 2024, doi: 10.1016/j.jbusres.2024.114760.
[19] J. D. Velasquez, “TechMiner: Analysis of bibliographic datasets using Python,” SoftwareX, vol. 23, Art. no. 101457, Jul. 2023, doi: 10.1016/j.softx.2023.101457.
[20] V. Pereira, M. P. Basilio, and C. H. T. Santos, “pyBibX -- A Python Library for Bibliometric and Scientometric Analysis Powered with Artificial Intelligence Tools,” Data Technol. Appl., vol. 59, no. 2, pp. 302–337, Apr. 2025, doi: 10.1108/DTA-08-2023-0461.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Denina Nastiti Putri Amani, Syopiansyah Jaya Putra, Qurrotul Aini
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
