Examining Hate Speech and Social Media in Indonesia: A Systematic Literature Review

Abstract

Hate speech and social media are increasingly prominent topics for scholars in Indonesia, yet prior reviews on the subject tend to remain within a single discipline. Building on the digital boom triggered by the Covid-19 pandemic, we systematically mapped the thematic trends of hate speech and social media research in Indonesia published between 2020 and 2025. We queried our corpus from ScienceDirect and utilised R, RStudio, and the Bibliometrix package as the primary tools to analyse the corpus. Our findings show that this remains a rapidly growing field (CAGR of 45.4% per year), with hate speech and offensive content detection forming its conceptual core, and deep learning and language models. Dominant methods cover deep learning and language models such as BERT and its Indonesian derivative. The thematic evolution shifted from situational triggers of Covid-19 pandemics to long-term structural issues such as hate speech itself and disinformation. Interpreting these patterns through Computer-Mediated Communication (CMC) and the Social Identity model of Deindividuation Effects (SIDE) as primary lenses, with Social Identity Theory (SIT) as a secondary lens, we argue that platform affordances and anonymity intensify group-based hostility in ways the literature has yet to explicitly theorise, particularly within Indonesia's distinct religious and linguistic context.