Datasets:
annotations_creators:
- expert-generated
language_creators:
- found
languages:
- en
licenses:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-classification-other-fake-news-detection
Dataset Card for [Dataset Name]
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://sites.cs.ucsb.edu/~william/
- Repository:
- Paper: https://arxiv.org/abs/1705.00648
- Leaderboard:
- Point of Contact:
Dataset Summary
LIAR is a dataset for fake news detection with 12.8K human labeled short statements from politifact.com's API, and each statement is evaluated by a politifact.com editor for its truthfulness. The distribution of labels in the LIAR dataset is relatively well-balanced: except for 1,050 pants-fire cases, the instances for all other labels range from 2,063 to 2,638. In each case, the labeler provides a lengthy analysis report to ground each judgment.
Supported Tasks and Leaderboards
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Languages
English.
Dataset Structure
Data Instances
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Data Fields
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Data Splits
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Dataset Creation
Curation Rationale
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Source Data
Initial Data Collection and Normalization
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Who are the source language producers?
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Annotations
Annotation process
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Who are the annotators?
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Personal and Sensitive Information
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Considerations for Using the Data
Social Impact of Dataset
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Discussion of Biases
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Other Known Limitations
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Additional Information
Dataset Curators
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Licensing Information
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Citation Information
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