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All Journal JUSIFOR : Jurnal Sistem Informasi dan Informatika
Qurrotul Aini
UIN Syarif Hidayatullah Jakarta, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science

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Tren Riset Deteksi Ujaran Kebencian: Analisis Bibliometrik 2020–2025 Denina Nastiti Putri Amani; Syopiansyah Jaya Putra; Qurrotul Aini
JUSIFOR : Jurnal Sistem Informasi dan Informatika Vol 5 No 1 (2026): JUSIFOR - Juni 2026
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/jusifor.v5i1.8769

Abstract

  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.
Bibliometrik Hate Speech: Tren Metode Penelitian dan Domain Implementasi Arrisa Aprilani Nurindah; Nida'ul Hasanati; Qurrotul Aini
JUSIFOR : Jurnal Sistem Informasi dan Informatika Vol 4 No 2 (2025): JUSIFOR - Desember 2025
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/jusifor.v4i2.8652

Abstract

This study aims to map the development of research related to hate speech through a bibliometric analysis of scientific publications indexed in Scopus. Using the keywords “hate speech” and “analysis,” a total of 2,009 publication metadata were obtained and analyzed using R Studio, Biblioshiny, and VOSviewer. The results indicate a significant increase in the number of publications, particularly during the 2021–2024 period, reflecting the growing academic attention toward hate speech issues. Domain analysis reveals that research is predominantly focused on the fields of Technology and Social Sciences, especially in the context of automated detection, social media, and the impact of digital society. Deep learning–based methods such as BERT and LSTM are the most frequently used techniques, in line with recent trends in Natural Language Processing (NLP). Furthermore, the co-occurrence analysis reveals the formation of several thematic clusters, including artificial intelligence, deep learning, multilingual hate speech, and large language models.
Co-Authors Arrisa Aprilani Nurindah Denina Nastiti Putri Amani Nida'ul Hasanati Syopiansyah Jaya Putra