Tren Riset Deteksi Ujaran Kebencian: Analisis Bibliometrik 2020–2025
DOI:
https://doi.org/10.70609/jusifor.v5i1.8769Kata Kunci:
ujaran kebencian, NLP, transformer, bibliometrik, multimodal, multibahasaAbstrak
Perkembangan media sosial yang sangat cepat meningkatkan risiko munculnya ujaran kebencian (hate speech), sehingga mendorong penelitian dalam bidang Natural Language Processing (NLP) untuk mengembangkan sistem deteksi otomatis yang lebih akurat. Selama satu dekade terakhir, pendekatan deteksi ujaran kebencian mengalami evolusi signifikan, mulai dari metode machine learning klasik hingga arsitektur deep learning dan model transformer yang lebih mutakhir. Namun, kajian bibliometrik yang memetakan perkembangan metode dan domain implementasi masih terbatas. Penelitian ini menganalisis 1.335 publikasi dari basis data Scopus untuk mengidentifikasi tren teknik penelitian (misalnya SVM, Naive Bayes, LSTM, dan keluarga BERT) serta domain penerapannya (Twitter, Facebook, YouTube, dan konteks multibahasa). Analisis dilakukan menggunakan Python melalui ekstraksi kata kunci dan visualisasi tren temporal. Hasil menunjukkan dominasi model transformer sejak 2020 dan pergeseran fokus dari teks tunggal menuju pendekatan multimodal dan multilingual. Studi ini menegaskan arah pengembangan riset menuju integrasi transformer, multibahasa, dan Explainable AI (XAI) untuk meningkatkan transparansi deteksi ujaran kebencian
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