Task Categories: text-classification
Languages: en
Multilinguality: monolingual
Size Categories: 10K<n<100K
Licenses: mit
Language Creators: found
Annotations Creators: expert-generated
Source Datasets: original
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Dataset Card for PUBHEALTH

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]


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:

Who are the source language producers?

[More Information Needed]


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

    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 = "",
    pages = "7740--7754",


Thanks to @bhavitvyamalik for adding this dataset.

Update on GitHub
Papers with Code

Models trained or fine-tuned on health_fact