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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
Languages
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
: astring
feature.title
: astring
feature.hyperpartisan
: abool
feature.url
: astring
feature.published_at
: astring
feature.
bypublisher
text
: astring
feature.title
: astring
feature.hyperpartisan
: abool
feature.url
: astring
feature.published_at
: astring
feature.bias
: a classification label, with possible values includingright
(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
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
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
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.
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