license: mit
configs:
- config_name: default
data_files:
- split: news
path: news.csv
size_categories:
- n<1K
task_categories:
- zero-shot-classification
- text-classification
language:
- en
Towards Real-Time Fake News Detection under Evidence Scarcity
Guangyu Wei*, Ke Han*, Yueming Lyu†, Yu Luo, Yue Jiang, Caifeng Shan, Nicu Sebe
(*Contribute equally, †Corresponding author)
👀 About RealTimeNews-2025
Conventional fake news datasets often comprise news that is several years old. Such instances are grounded in a wealth of post-hoc evidence, including public discussions, official statements, and scientific articles. To advance research on real-time fake news detection, we introduce a new benchmark, RealTimeNews-25, consisting of 3,487 news articles collected between June 2024 and September 2025. The dataset covers recent and rapidly evolving events characterized by limited supporting evidence, providing a challenging and timely benchmark for evaluating model robustness in real-world, time-sensitive scenarios.
We have publicly released the RealTimeNews-2025 Dataset. Researchers can now download and use it by completing this form.
⚙️ Dataset Format
The dataset is structured as follows:
├── data
├── news
└── news.json
├── imgs
├── 0.png
├── 1.jpg
├── 2.png
└── ... # {id}.jpg/png/webp
❤️ Citation
Please cite the paper as follows if you use the RealTimeNews-2025:
@misc{wei2025realtimefakenewsdetection,
title={Towards Real-Time Fake News Detection under Evidence Scarcity},
author={Guangyu Wei and Ke Han and Yueming Lyu and Yu Luo and Yue Jiang and Caifeng Shan and Nicu Sebe},
year={2025},
eprint={2510.11277},
archivePrefix={arXiv},
primaryClass={cs.CL},
}