A Multi-Labeled Dataset for Indonesian Discourse: Examining Toxicity, Polarization, and Demographics Information

• URL: http://arxiv.org/abs/2503.00417v1
• Date: Sat, 01 Mar 2025 09:33:10 GMT
• Title: A Multi-Labeled Dataset for Indonesian Discourse: Examining Toxicity, Polarization, and Demographics Information
• Authors: Lucky Susanto, Musa Wijanarko, Prasetia Pratama, Zilu Tang, Fariz Akyas, Traci Hong, Ika Idris, Alham Aji, Derry Wijaya,
• Abstract summary: As the world's third-largest democracy, Indonesia faces growing concerns about the interplay between political polarization and online toxicity.<n>Previous NLP research has not fully explored the relationship between toxicity and polarization.<n>We present a novel multi-label Indonesian dataset that incorporates toxicity, polarization, and annotator demographic information.
• Score: 2.8697660350772063
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Polarization is defined as divisive opinions held by two or more groups on substantive issues. As the world's third-largest democracy, Indonesia faces growing concerns about the interplay between political polarization and online toxicity, which is often directed at vulnerable minority groups. Despite the importance of this issue, previous NLP research has not fully explored the relationship between toxicity and polarization. To bridge this gap, we present a novel multi-label Indonesian dataset that incorporates toxicity, polarization, and annotator demographic information. Benchmarking this dataset using BERT-base models and large language models (LLMs) shows that polarization information enhances toxicity classification, and vice versa. Furthermore, providing demographic information significantly improves the performance of polarization classification.

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