|
--- |
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license: cc-by-nc-4.0 |
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task_categories: |
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- text-classification |
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language: |
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- en |
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tags: |
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- event-forecasting |
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- international-relations |
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- geopolitics |
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- text-classification |
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pretty_name: WORLDREP |
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size_categories: |
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- 100K<n<1M |
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dataset_info: |
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features: |
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- name: EventID |
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dtype: string |
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- name: SourceURL |
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dtype: string |
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- name: DATE |
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dtype: string |
|
- name: CONTENT |
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dtype: string |
|
- name: Country1 |
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dtype: string |
|
- name: Country2 |
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dtype: string |
|
- name: Score |
|
dtype: float64 |
|
splits: |
|
- name: train |
|
num_bytes: 19348381 |
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num_examples: 147697 |
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download_size: 2949164 |
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dataset_size: 19348381 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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|
|
|
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# WORLDREP: A Dataset for Forecasting Future International Events |
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WORLDREP (**WORLD Relationship and Event Prediction**) is a high-quality dataset designed for predicting future international events based on textual information, such as news articles. It provides the relationships between countries with numerical scores ranging from **0.0 (cooperation)** to **1.0 (conflict)**. |
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|
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## Dataset Overview |
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|
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This dataset was introduced in: |
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**Forecasting Future International Events: A Reliable Dataset for Text-Based Event Modeling** ([Link](https://arxiv.org/abs/2411.14042)) |
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|
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### **Dataset Structure** |
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| Column | Description | |
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|--------------|-----------------------------------------------------------------------------| |
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| `EventID` | Unique identifier for the event | |
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| `SourceURL` | URL of the news article reporting the event | |
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| `DATE` | Publication date of the article in `YYYYMMDDHHMMSS` format | |
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| `CONTENT` | Content of the news article | |
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| `Country1` | The first country involved in the event | |
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| `Country2` | The second country involved in the event | |
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| `Score` | Numerical value (0.0-1.0) representing the relationship between countries. A score close to **0.0** indicates **cooperation**, while a score close to **1.0** indicates **conflict**. | |
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|
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## Applications |
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- Predicting future international events |
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- Understanding geopolitical trends |
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- Training machine learning models for event forecasting |
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|
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## License |
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This dataset is licensed under the [Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/). |
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|
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## Citation |
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If you use this dataset, please cite the corresponding paper: |
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|
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``` |
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@inproceedings{gwak2024worldrep, |
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title={Forecasting Future International Events: A Reliable Dataset for Text-Based Event Modeling}, |
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author={Daehoon Gwak, Junwoo Park, Minho Park, Chaehun Park, Hyunchan Lee, Edward Choi and Jaegul Choo}, |
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booktitle={EMNLP Findings}, |
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year={2024} |
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} |
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``` |
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|
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### Related Resources |
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- [Paper](https://arxiv.org/abs/2411.14042) |
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- [GitHub Repository for WORLDREP](https://github.com/eogns282/WORLDREP) |
