A Multi-Labeled Dataset for Indonesian Discourse: Examining Toxicity, Polarization, and Demographics Information
This addresses the problem of online toxicity and political polarization in Indonesia, particularly affecting vulnerable minority groups, but is incremental as it builds on existing NLP methods with new data.
The researchers tackled the lack of understanding between toxicity and polarization in Indonesian online discourse by creating a multi-label dataset that includes toxicity, polarization, and demographic information, and found that incorporating polarization improves toxicity classification and demographic data enhances polarization classification.
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.