| --- |
| license: apache-2.0 |
| task_categories: |
| - text-classification |
| language: |
| - id |
| tags: |
| - Hate Speech Classification |
| - Toxicity Classification |
| - Demographic Information |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: main |
| data_files: |
| - split: main |
| path: |
| - "indotoxic2024_annotated_data_v2_final.jsonl" |
| - config_name: annotator |
| data_files: |
| - split: annotator |
| path: |
| - "indotoxic2024_annotator_demographic_data_v2_final.jsonl" |
| --- |
| |
| ``` |
| Notice: We added new data and restructured the dataset on 31st October 2024 (GMT+7) |
| Changes: |
| - Group unique texts together |
| - 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. |
| - Added Polarized column |
| |
| Notice 2: We rename the dataset from IndoToxic2024 to IndoDiscourse |
| ``` |
|
|
| # A Multi-Labeled Dataset for Indonesian Discourse: Examining Toxicity, Polarization, and Demographics Information |
|
|
| ## Dataset Overview |
|
|
| 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. |
|
|
| ## Dataset Statistics |
|
|
| - **Total annotated texts:** **28,477** |
| - **Platforms:** X (formerly Twitter), Facebook, Instagram, and news articles |
| - **Timeframe:** September 2023 – January 2024 |
| - **Annotators:** 29 individuals from diverse demographic backgrounds |
|
|
| ### Label Distribution - For Experiments |
|
|
| | Label | Count | |
| |-------------|-------| |
| | **Toxic** | 2,156 (balanced) | |
| | **Non-Toxic** | 6,468 (balanced) | |
| | **Polarized** | 3,811 (balanced) | |
| | **Non-Polarized** | 11,433 (balanced) | |
|
|
| ## Dataset Structure |
|
|
| 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. |
|
|
| ### Features: |
| - `text`: The Indonesian social media or news text |
| - `toxicity`: List of toxicity annotations (1 = Toxic, 0 = Non-Toxic) |
| - `polarization`: List of polarization annotations (1 = Polarized, 0 = Non-Polarized) |
| - `annotators_id`: List of annotator_id that annotate the text (anonymized) -- Refer to `annotator` subset for each annotator_id's demographic informatino |
|
|
| ## Baseline Model Performance |
|
|
|  |
|
|
| ### Experiment Code |
|
|
| [Notebook for Toxicity Related Experiment](https://huggingface.co/datasets/Exqrch/IndoDiscourse/blob/main/IndoDiscourse%20-%20Toxicity%20Related%20Experiment%20Code.ipynb) |
|
|
|
|
| ### Key Results: |
|
|
| We benchmarked IndoDiscourse using **BERT-based models** and **large language models (LLMs)**. The results indicate that: |
|
|
| - **BERT-based models outperform 0-shot LLMs**, with **IndoBERTweet** achieving the highest accuracy. |
| - **Polarization detection is harder than toxicity detection**, as evidenced by lower recall scores. |
| - **Demographic information improves classification**, especially for polarization detection. |
|
|
| ### Additional Findings: |
| - **Polarization and toxicity are correlated**: Using polarization as a feature improves toxicity detection, and vice versa. |
| - **Demographic-aware models perform better for polarization detection**: Including coder demographics boosts classification performance. |
| - **Wisdom of the crowd**: Texts labeled by multiple annotators lead to higher recall in toxicity detection. |
|
|
| ## Ethical Considerations |
|
|
| - **Data Privacy**: All annotator demographic data is anonymized. |
| - **Use Case**: This dataset is released **for research purposes only** and should not be used for surveillance or profiling. |
|
|
| ## Citation |
|
|
| If you use IndoDiscourse, please cite: |
|
|
| ```bibtex |
| @misc{susanto2025multilabeleddatasetindonesiandiscourse, |
| title={A Multi-Labeled Dataset for Indonesian Discourse: Examining Toxicity, Polarization, and Demographics Information}, |
| 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}, |
| year={2025}, |
| eprint={2503.00417}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2503.00417}, |
| }``` |
|
|
