README.md

---

---

Dataset Card for "hyperpartisan_news_detection"

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits Sample Size
• 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://pan.webis.de/semeval19/semeval19-web/
• Repository: More Information Needed
• Paper: More Information Needed
• Point of Contact: More Information Needed
• Size of downloaded dataset files: 957.68 MB
• Size of the generated dataset: 5354.14 MB
• Total amount of disk used: 6311.82 MB

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

More Information Needed

Languages

More Information Needed

Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances

byarticle

• Size of downloaded dataset files: 0.95 MB
• Size of the generated dataset: 2.67 MB
• Total amount of disk used: 3.63 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: 956.72 MB
• Size of the generated dataset: 5351.47 MB
• Total amount of disk used: 6308.19 MB

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 Sample Size

byarticle

	train
byarticle	645

bypublisher

	train	validation
bypublisher	600000	600000

Dataset Creation

Curation Rationale

More Information Needed

Source Data

More Information Needed

Annotations

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

@article{kiesel2019data,
  title={Data for pan at semeval 2019 task 4: Hyperpartisan news detection},
  author={Kiesel, Johannes and Mestre, Maria and Shukla, Rishabh and Vincent, Emmanuel and Corney, David and Adineh, Payam and Stein, Benno and Potthast, Martin},
  year={2019}
}

Contributions

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