task_categories:text-classification annotations_creators:crowdsourced annotations_creators:expert-generated language_creators:found multilinguality:monolingual size_categories:1M<n<10M source_datasets:original language:en license:cc-by-4.0 bias-classification region:us
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.
@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.",
}
---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: HyperpartisanNewsDetection
tags:
- bias-classification
dataset_info:
- config_name: byarticle
  features:
  - name: text
    dtype: string
  - name: title
    dtype: string
  - name: hyperpartisan
    dtype: bool
  - name: url
    dtype: string
  - name: published_at
    dtype: string
  splits:
  - name: train
    num_bytes: 2803943
    num_examples: 645
  download_size: 1000352
  dataset_size: 2803943
- config_name: bypublisher
  features:
  - name: text
    dtype: string
  - name: title
    dtype: string
  - name: hyperpartisan
    dtype: bool
  - name: url
    dtype: string
  - name: published_at
    dtype: string
  - name: bias
    dtype:
      class_label:
        names:
          '0': right
          '1': right-center
          '2': least
          '3': left-center
          '4': left
  splits:
  - name: train
    num_bytes: 2805711609
    num_examples: 600000
  - name: validation
    num_bytes: 960356598
    num_examples: 150000
  download_size: 1003195420
  dataset_size: 5611423218
---

# Dataset Card for "hyperpartisan_news_detection"

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [https://pan.webis.de/semeval19/semeval19-web/](https://pan.webis.de/semeval19/semeval19-web/)
- **Repository:** https://github.com/pan-webis-de/pan-code/tree/master/semeval19
- **Paper:** https://aclanthology.org/S19-2145
- **Data:** https://doi.org/10.5281/zenodo.1489920
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 1.00 GB
- **Size of the generated dataset:** 5.61 GB
- **Total amount of disk used:** 6.62 GB

### 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](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Languages

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## 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

|         |train|
|---------|----:|
|byarticle|  645|

#### bypublisher

|           |train |validation|
|-----------|-----:|---------:|
|bypublisher|600000|    150000|

## Dataset Creation

### Curation Rationale

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the source language producers?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Annotations

#### Annotation process

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the annotators?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Personal and Sensitive Information

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Discussion of Biases

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Other Known Limitations

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Additional Information

### Dataset Curators

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Licensing Information

The collection (including labels) are licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).

### 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](https://github.com/thomwolf), [@ghomasHudson](https://github.com/ghomasHudson) for adding this dataset.