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Datasets:
DomLoyer
/
liar

Tasks:
Text Classification
Languages:
English
Size:
10K<n<100K
ArXiv:
Tags:
fake-news-detection
License:
Dataset card Files
xet
 Community
liar
File size: 4,097 Bytes
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---
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.