Bibliometrik Hate Speech: Tren Metode Penelitian dan Domain Implementasi - Nurindah, Arrisa Aprilani; Hasanati, Nida'ul; Aini, Qurrotul (Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang, 2025)
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.