| ### Dataset Summary | |
| The data provided is (headline, body, stance) instances, where the stance is one of {unrelated, discuss, agree, disagree}. | |
| **Input** | |
| * A headline and a body text - either from the same news article or from two different articles. | |
| **Output** | |
| * Classify the stance of the body text relative to the claim made in the headline into one of four categories: | |
| * Agrees: The body text agrees with the headline. | |
| * Disagrees: The body text disagrees with the headline. | |
| * Discusses: The body text discuss the same topic as the headline, but does not take a position | |
| * Unrelated: The body text discusses a different topic than the headline | |
| The distribution of Stance classes in the entire dataset is as follows: | |
| | rows | unrelated | discuss | agree | disagree | | |
| |---------|-----------|---------|-----------|----------- | | |
| | 49972 | 0.73131 | 0.17828 | 0.0736012 | 0.016809 | | |
| ### Source Data | |
| [FNC-1 Official webpage.](http://www.fakenewschallenge.org/) | |
| - annotations_creators: found | |
| - language_creators: found | |
| - languages: en-US | |
| - licenses: apache-2.0 | |
| - multilingualism: monolingual | |
| - pretty_name: FNC-1 | |
| - size_categories: unknown | |
| - source_datasets: original | |
| - task_categories:text-classification | |
| - task_ids | |
| - multi-class-classification | |
| - natural-language-inference | |
| - multi-label-classification | |
| - intent-classification |