Dataset Card for "hyperpartisan_news_detection"

Dataset Summary

Hyperpartisan News Detection was a dataset created for PAN @ SemEval 2019 Task 4. Given a news article text, decide whether it follows a hyperpartisan argumentation, i.e., whether it exhibits blind, prejudiced, or unreasoning allegiance to one party, faction, cause, or person.

There are 2 parts:

• byarticle: Labeled through crowdsourcing on an article basis. The data contains only articles for which a consensus among the crowdsourcing workers existed.
• bypublisher: Labeled by the overall bias of the publisher as provided by BuzzFeed journalists or MediaBiasFactCheck.com.

Supported Tasks and Leaderboards

More Information Needed

Languages

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Dataset Structure

Data Instances

byarticle

• Size of downloaded dataset files: 1.00 MB
• Size of the generated dataset: 2.80 MB
• Total amount of disk used: 3.81 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "hyperpartisan": true,
    "published_at": "2020-01-01",
    "text": "\"<p>This is a sample article which will contain lots of text</p>\\n    \\n<p>Lorem ipsum dolor sit amet, consectetur adipiscing el...",
    "title": "Example article 1",
    "url": "http://www.example.com/example1"
}

bypublisher

• Size of downloaded dataset files: 1.00 GB
• Size of the generated dataset: 5.61 GB
• Total amount of disk used: 6.61 GB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "bias": 3,
    "hyperpartisan": false,
    "published_at": "2020-01-01",
    "text": "\"<p>This is a sample article which will contain lots of text</p>\\n    \\n<p>Phasellus bibendum porta nunc, id venenatis tortor fi...",
    "title": "Example article 4",
    "url": "https://example.com/example4"
}

Data Fields

The data fields are the same among all splits.

byarticle

• text: a string feature.
• title: a string feature.
• hyperpartisan: a bool feature.
• url: a string feature.
• published_at: a string feature.

bypublisher

• text: a string feature.
• title: a string feature.
• hyperpartisan: a bool feature.
• url: a string feature.
• published_at: a string feature.
• bias: a classification label, with possible values including right (0), right-center (1), least (2), left-center (3), left (4).

Data Splits

byarticle

bypublisher

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

More Information Needed

Licensing Information

The collection (including labels) are licensed under a Creative Commons Attribution 4.0 International License.

Citation Information

@inproceedings{kiesel-etal-2019-semeval,
    title = "{S}em{E}val-2019 Task 4: Hyperpartisan News Detection",
    author = "Kiesel, Johannes  and
      Mestre, Maria  and
      Shukla, Rishabh  and
      Vincent, Emmanuel  and
      Adineh, Payam  and
      Corney, David  and
      Stein, Benno  and
      Potthast, Martin",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S19-2145",
    doi = "10.18653/v1/S19-2145",
    pages = "829--839",
    abstract = "Hyperpartisan news is news that takes an extreme left-wing or right-wing standpoint. If one is able to reliably compute this meta information, news articles may be automatically tagged, this way encouraging or discouraging readers to consume the text. It is an open question how successfully hyperpartisan news detection can be automated, and the goal of this SemEval task was to shed light on the state of the art. We developed new resources for this purpose, including a manually labeled dataset with 1,273 articles, and a second dataset with 754,000 articles, labeled via distant supervision. The interest of the research community in our task exceeded all our expectations: The datasets were downloaded about 1,000 times, 322 teams registered, of which 184 configured a virtual machine on our shared task cloud service TIRA, of which in turn 42 teams submitted a valid run. The best team achieved an accuracy of 0.822 on a balanced sample (yes : no hyperpartisan) drawn from the manually tagged corpus; an ensemble of the submitted systems increased the accuracy by 0.048.",
}

Contributions

Thanks to @thomwolf, @ghomasHudson for adding this dataset.