Datasets:

ucsbnlp
/

liar

	---
	annotations_creators:
	- expert-generated
	language_creators:
	- found
	language:
	- en
	license:
	- unknown
	multilinguality:
	- monolingual
	size_categories:
	- 10K<n<100K
	source_datasets:
	- original
	task_categories:
	- text-classification
	task_ids: []
	paperswithcode_id: liar
	pretty_name: LIAR
	tags:
	- fake-news-detection
	dataset_info:
	features:
	- name: id
	dtype: string
	- name: label
	dtype:
	class_label:
	names:
	'0': 'false'
	'1': half-true
	'2': mostly-true
	'3': 'true'
	'4': barely-true
	'5': pants-fire
	- name: statement
	dtype: string
	- name: subject
	dtype: string
	- name: speaker
	dtype: string
	- name: job_title
	dtype: string
	- name: state_info
	dtype: string
	- name: party_affiliation
	dtype: string
	- name: barely_true_counts
	dtype: float32
	- name: false_counts
	dtype: float32
	- name: half_true_counts
	dtype: float32
	- name: mostly_true_counts
	dtype: float32
	- name: pants_on_fire_counts
	dtype: float32
	- name: context
	dtype: string
	splits:
	- name: train
	num_bytes: 2730651
	num_examples: 10269
	- name: test
	num_bytes: 341414
	num_examples: 1283
	- name: validation
	num_bytes: 341592
	num_examples: 1284
	download_size: 1013571
	dataset_size: 3413657
	train-eval-index:
	- config: default
	task: text-classification
	task_id: multi_class_classification
	splits:
	train_split: train
	eval_split: test
	col_mapping:
	statement: text
	label: target
	metrics:
	- type: accuracy
	name: Accuracy
	- type: f1
	name: F1 macro
	args:
	average: macro
	- type: f1
	name: F1 micro
	args:
	average: micro
	- type: f1
	name: F1 weighted
	args:
	average: weighted
	- type: precision
	name: Precision macro
	args:
	average: macro
	- type: precision
	name: Precision micro
	args:
	average: micro
	- type: precision
	name: Precision weighted
	args:
	average: weighted
	- type: recall
	name: Recall macro
	args:
	average: macro
	- type: recall
	name: Recall micro
	args:
	average: micro
	- type: recall
	name: Recall weighted
	args:
	average: weighted
	---

	# Dataset Card for [Dataset Name]

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## 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

	[More Information Needed]

	### Languages

	English.

	## Dataset Structure

	### Data Instances

	[More Information Needed]

	### Data Fields

	[More Information Needed]

	### Data Splits

	[More Information Needed]

	## Dataset Creation

	### Curation Rationale

	[More Information Needed]

	### Source Data

	#### Initial Data Collection and Normalization

	[More Information Needed]

	#### Who are the source language producers?

	[More Information Needed]

	### Annotations

	#### Annotation process

	[More Information Needed]

	#### Who are the annotators?

	[More Information Needed]

	### Personal and Sensitive Information

	[More Information Needed]

	## Considerations for Using the Data

	### Social Impact of Dataset

	[More Information Needed]

	### Discussion of Biases

	[More Information Needed]

	### Other Known Limitations

	[More Information Needed]

	## Additional Information

	### Dataset Curators

	[More Information Needed]

	### Licensing Information

	[More Information Needed]

	### Citation Information

	[More Information Needed]

	### Contributions

	Thanks to [@hugoabonizio](https://github.com/hugoabonizio) for adding this dataset.