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--- |
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license: apache-2.0 |
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task_categories: |
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- text-classification |
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language: |
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- id |
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tags: |
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- Hate Speech Classification |
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- Toxicity Classification |
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- Demographic Information |
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size_categories: |
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- 10K<n<100K |
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configs: |
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- config_name: main |
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data_files: |
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- split: main |
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path: |
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- "indotoxic2024_annotated_data_v2_final.jsonl" |
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- config_name: annotator |
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data_files: |
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- split: annotator |
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path: |
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- "indotoxic2024_annotator_demographic_data_v2_final.jsonl" |
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--- |
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``` |
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Notice: We added new data and restructured the dataset on 31st October 2024 (GMT+7) |
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Changes: |
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- Group unique texts together |
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- The annotators of a text are now set as a list of annotator_id. Each respective column is a list of the same size of annotators_id. |
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- Added Polarized column |
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Notice 2: We rename the dataset from IndoToxic2024 to IndoDiscourse |
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``` |
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# A Multi-Labeled Dataset for Indonesian Discourse: Examining Toxicity, Polarization, and Demographics Information |
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## Dataset Overview |
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IndoToxic2024 is a multi-labeled dataset designed to analyze online discourse in Indonesia, focusing on **toxicity, polarization, and annotator demographic information**. This dataset provides insights into the growing political and social divisions in Indonesia, particularly in the context of the **2024 presidential election**. Unlike previous datasets, IndoToxic2024 offers a **multi-label annotation** framework, enabling nuanced research on the interplay between toxicity and polarization. |
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## Dataset Statistics |
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- **Total annotated texts:** **28,477** |
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- **Platforms:** X (formerly Twitter), Facebook, Instagram, and news articles |
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- **Timeframe:** September 2023 – January 2024 |
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- **Annotators:** 29 individuals from diverse demographic backgrounds |
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### Label Distribution - For Experiments |
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| Label | Count | |
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|-------------|-------| |
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| **Toxic** | 2,156 (balanced) | |
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| **Non-Toxic** | 6,468 (balanced) | |
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| **Polarized** | 3,811 (balanced) | |
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| **Non-Polarized** | 11,433 (balanced) | |
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## Dataset Structure |
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The dataset consists of texts labeled for **toxicity and polarization**, along with **annotator demographics**. Each text is annotated by at least one coder, with **44.6% of texts receiving multiple annotations**. Annotations were aggregated using majority voting, excluding texts with perfect disagreement. |
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### Features: |
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- `text`: The Indonesian social media or news text |
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- `toxicity`: List of toxicity annotations (1 = Toxic, 0 = Non-Toxic) |
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- `polarization`: List of polarization annotations (1 = Polarized, 0 = Non-Polarized) |
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- `annotators_id`: List of annotator_id that annotate the text (anonymized) -- Refer to `annotator` subset for each annotator_id's demographic informatino |
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## Baseline Model Performance |
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### Experiment Code |
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[Notebook for Toxicity Related Experiment](https://huggingface.co/datasets/Exqrch/IndoDiscourse/blob/main/IndoDiscourse%20-%20Toxicity%20Related%20Experiment%20Code.ipynb) |
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### Key Results: |
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We benchmarked IndoDiscourse using **BERT-based models** and **large language models (LLMs)**. The results indicate that: |
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- **BERT-based models outperform 0-shot LLMs**, with **IndoBERTweet** achieving the highest accuracy. |
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- **Polarization detection is harder than toxicity detection**, as evidenced by lower recall scores. |
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- **Demographic information improves classification**, especially for polarization detection. |
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### Additional Findings: |
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- **Polarization and toxicity are correlated**: Using polarization as a feature improves toxicity detection, and vice versa. |
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- **Demographic-aware models perform better for polarization detection**: Including coder demographics boosts classification performance. |
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- **Wisdom of the crowd**: Texts labeled by multiple annotators lead to higher recall in toxicity detection. |
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## Ethical Considerations |
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- **Data Privacy**: All annotator demographic data is anonymized. |
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- **Use Case**: This dataset is released **for research purposes only** and should not be used for surveillance or profiling. |
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## Citation |
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If you use IndoDiscourse, please cite: |
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```bibtex |
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@misc{susanto2025multilabeleddatasetindonesiandiscourse, |
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title={A Multi-Labeled Dataset for Indonesian Discourse: Examining Toxicity, Polarization, and Demographics Information}, |
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author={Lucky Susanto and Musa Wijanarko and Prasetia Pratama and Zilu Tang and Fariz Akyas and Traci Hong and Ika Idris and Alham Aji and Derry Wijaya}, |
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year={2025}, |
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eprint={2503.00417}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2503.00417}, |
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}``` |
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