metadata
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)
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).
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}
}