NeuralNews
Introduced by Tan et al. in Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News
NeuralNews is a dataset for machine-generated news detection. It consists of human-generated and machine-generated articles. The human-generated articles are extracted from the GoodNews dataset, which is extracted from the New York Times. It contains 4 types of articles:
- Real Articles and Real Captions
- Real Articles and Generated Captions
- Generated Articles and Real Captions
- Generated Articles and Generated Captions
In total, it contains about 32K samples of each article type (resulting in about 128K total).
Source: Detecting Cross-Modal Inconsistency to Defend Against Neural Fake NewsPapers
| Paper | Code | Results | Date | Stars |
|---|
Dataset Loaders
No data loaders found. You can
submit your data loader here.
No data loaders found. You can
submit your data loader here.
