README.md

metadata

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 Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • 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 for adding this dataset.