DomLoyer/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.

### Contributions

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