Hate speech propagated online is a complex issue, deeply influenced by the cultural, historical, and societal contexts of both the perpetrator and the target. Consequently, the development of a universally robust hate speech classifier for diverse social media texts remains a challenging and unsolved task. The lack of mechanisms to track the spread and severity of hate speech further complicates the formulation of effective solutions. In response to this, to monitor hate speech in Indonesia during the recent 2024 presidential election, we have employed advanced Natural Language Processing (NLP) technologies to create an improved hate speech classifier tailored for a narrower subset of texts; specifically, texts that target minority groups that have historically been the targets of hate speech in Indonesia. Our focus is on texts that mention these seven vulnerable minority groups in Indonesia: Shia, Ahmadiyyah, Christian, LGBTQ+, Chinese, Jewish, and people with disabilities. The insights gained from our dashboard have assisted stakeholders in devising more effective strategies to counteract hate speech. Notably, our dashboard has persuaded the General Election Supervisory Body in Indonesia (BAWASLU) to collaborate with our institution and the Alliance of Independent Journalists (AJI) to monitor social media hate speech in vulnerable areas in the country known for hate speech dissemination or hate-related violence in the upcoming Indonesian regional elections. This dashboard is available online at \url{https://www.aji.or.id/}

Monitoring Hate Speech in Indonesia: An NLP-based Classification of Social Media Texts

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Monitoring Hate Speech in Indonesia: An NLP-based Classification of Social Media Texts

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