Language and Symbols in Indonesian Political Hate Speech: A Critical Discourse Analysis
DOI:
https://doi.org/10.61320/jolcc.v3i2.321-343Keywords:
critical discourse analysis, emoji, hate speech, lexical borrowing, political discourseAbstract
Political hate speech in Indonesian social media has grown stronger, and its force is typically produced through multimodal resources rather than words alone in everyday online political conversations. Prior studies mainly investigate verbal aggression, emojis, and lexical borrowing separately; therefore, the way these resources interact in political hate remains unclear. This study bridges that gap by studying how English lexical borrowing and emojis combine to build Indonesian political hate speech and reproduce ideology. Applying a qualitative design, thirty publicly viewable hate-speech comments were purposively sampled from X/Twitter, Instagram, and TikTok (ten per site). The dataset was examined with Teun A. van Dijk’s Critical Discourse Analysis, relating textual structures (macrostructure, superstructure, microstructure) to social cognition and social context. Borrowed items were tagged as loanwords, loan blends, or semantic loans, and emojis were coded by pragmatic function (e.g., sarcasm, mocking, disgust). Findings demonstrate that multimodal hate speech is dominant: comments containing borrowing and emojis are most frequent, while borrowing-only remarks exceed emoji-only ones. Direct English loanwords serve as high-impact evaluative instruments, while emojis systematically increase posture, notably through sarcasm/mockery and disgust-based dehumanization of offenders. At the cognitive level, these tools continuously enact dehumanization as the strongest ideology, alongside anti-democratic and anti-elite/systemic-betrayal ideologies that legitimate contempt and divisiveness in online politics. Thus, Indonesian political hate speech acts as a coordinated verbal–symbolic approach. Although based on a small qualitative dataset typical of CDA, the analysis avoids overinterpreting emojis or borrowed forms by identifying ideological meaning only when these elements recur consistently across hostile contexts, ensuring that stylistic choices are distinguished from multimodal cues that genuinely contribute to political hate. Prevention, detection, and digital-literacy efforts must treat emojis and borrowed terminology as key bearers of political violence, not peripheral indications, and future studies should investigate these tendencies in bigger corpora, across regions, and during election cycles.
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