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---
license: cc-by-nc-4.0
task_categories:
- text-classification
language:
- en
tags:
- event-forecasting
- international-relations
- geopolitics
- text-classification
pretty_name: WORLDREP
size_categories:
- 100K<n<1M
dataset_info:
features:
- name: EventID
dtype: string
- name: SourceURL
dtype: string
- name: DATE
dtype: string
- name: CONTENT
dtype: string
- name: Country1
dtype: string
- name: Country2
dtype: string
- name: Score
dtype: float64
splits:
- name: train
num_bytes: 19348381
num_examples: 147697
download_size: 2949164
dataset_size: 19348381
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# WORLDREP: A Dataset for Forecasting Future International Events
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)**.
## Dataset Overview
This dataset was introduced in:
**Forecasting Future International Events: A Reliable Dataset for Text-Based Event Modeling** ([Link](https://arxiv.org/abs/2411.14042))
### **Dataset Structure**
| Column | Description |
|--------------|-----------------------------------------------------------------------------|
| `EventID` | Unique identifier for the event |
| `SourceURL` | URL of the news article reporting the event |
| `DATE` | Publication date of the article in `YYYYMMDDHHMMSS` format |
| `CONTENT` | Content of the news article |
| `Country1` | The first country involved in the event |
| `Country2` | The second country involved in the event |
| `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**. |
## Applications
- Predicting future international events
- Understanding geopolitical trends
- Training machine learning models for event forecasting
## License
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/).
## Citation
If you use this dataset, please cite the corresponding paper:
```
@inproceedings{gwak2024worldrep,
title={Forecasting Future International Events: A Reliable Dataset for Text-Based Event Modeling},
author={Daehoon Gwak, Junwoo Park, Minho Park, Chaehun Park, Hyunchan Lee, Edward Choi and Jaegul Choo},
booktitle={EMNLP Findings},
year={2024}
}
```
### Related Resources
- [Paper](https://arxiv.org/abs/2411.14042)
- [GitHub Repository for WORLDREP](https://github.com/eogns282/WORLDREP) |