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Datasets:
ImperialCollegeLondon
/
health_fact

Tasks:
Text Classification
Sub-tasks:
fact-checking
multi-class-classification
Languages:
English
Size:
10K<n<100K
ArXiv:
License:
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health_fact / README.md
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metadata
annotations_creators:
  - expert-generated
language_creators:
  - found
language:
  - en
license:
  - mit
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - fact-checking
  - multi-class-classification
paperswithcode_id: pubhealth
pretty_name: PUBHEALTH
dataset_info:
  features:
    - name: claim_id
      dtype: string
    - name: claim
      dtype: string
    - name: date_published
      dtype: string
    - name: explanation
      dtype: string
    - name: fact_checkers
      dtype: string
    - name: main_text
      dtype: string
    - name: sources
      dtype: string
    - name: label
      dtype:
        class_label:
          names:
            '0': 'false'
            '1': mixture
            '2': 'true'
            '3': unproven
    - name: subjects
      dtype: string
  splits:
    - name: train
      num_bytes: 53985377
      num_examples: 9832
    - name: test
      num_bytes: 6825221
      num_examples: 1235
    - name: validation
      num_bytes: 6653044
      num_examples: 1225
  download_size: 24892660
  dataset_size: 67463642
train-eval-index:
  - config: default
    task: text-classification
    task_id: multi_class_classification
    splits:
      train_split: train
      eval_split: test
    col_mapping:
      claim: 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 PUBHEALTH

Table of Contents

Dataset Description

Dataset Summary

PUBHEALTH is a comprehensive dataset for explainable automated fact-checking of public health claims. Each instance in the PUBHEALTH dataset has an associated veracity label (true, false, unproven, mixture). Furthermore each instance in the dataset has an explanation text field. The explanation is a justification for which the claim has been assigned a particular veracity label.

Supported Tasks and Leaderboards

[More Information Needed]

Languages

The text in the dataset is in English.

Dataset Structure

Data Instances

The following is an example instance of the PUBHEALTH dataset:

Field Example
claim Expired boxes of cake and pancake mix are dangerously toxic.
explanation What's True: Pancake and cake mixes that contain mold can cause life-threatening allergic reactions. What's False: Pancake and cake mixes that have passed their expiration dates are not inherently dangerous to ordinarily healthy people, and the yeast in packaged baking products does not "over time develops spores."
label mixture
author(s) David Mikkelson
date published April 19, 2006
tags food, allergies, baking, cake
main_text In April 2006, the experience of a 14-year-old who had eaten pancakes made from a mix that had gone moldy was described in the popular newspaper column Dear Abby. The account has since been circulated widely on the Internet as scores of concerned homemakers ponder the safety of the pancake and other baking mixes lurking in their larders [...]
evidence sources [1] Bennett, Allan and Kim Collins. “An Unusual Case of Anaphylaxis: Mold in Pancake Mix.” American Journal of Forensic Medicine & Pathology. September 2001 (pp. 292-295). [2] Phillips, Jeanne. “Dear Abby.” 14 April 2006 [syndicated column].

Data Fields

Mentioned above in data instances.

Data Splits

# Instances
train.tsv 9832
dev.tsv 1221
test.tsv 1235
total 12288

Dataset Creation

Curation Rationale

The dataset was created to explore fact-checking of difficult to verify claims i.e., those which require expertise from outside of the journalistics domain, in this case biomedical and public health expertise.

It was also created in response to the lack of fact-checking datasets which provide gold standard natural language explanations for verdicts/labels.

Source Data

Initial Data Collection and Normalization

The dataset was retrieved from the following fact-checking, news reviews and news websites:

URL Type
http://snopes.com/ fact-checking
http://politifact.com/ fact-checking
http://truthorfiction.com/ fact-checking
https://www.factcheck.org/ fact-checking
https://fullfact.org/ fact-checking
https://apnews.com/ news
https://uk.reuters.com/ news
https://www.healthnewsreview.org/ health news review

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

Not to our knowledge, but if it is brought to our attention that we are mistaken we will make the appropriate corrections to the dataset.

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

The dataset was created by Neema Kotonya, and Francesca Toni, for their research paper "Explainable Automated Fact-Checking for Public Health Claims" presented at EMNLP 2020.

Licensing Information

MIT License

Citation Information 

@inproceedings{kotonya-toni-2020-explainable,
    title = "Explainable Automated Fact-Checking for Public Health Claims",
    author = "Kotonya, Neema  and
      Toni, Francesca",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.623",
    pages = "7740--7754",
}

Contributions

Thanks to @bhavitvyamalik for adding this dataset.