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  1. Data for PAN at SemEval 2019 Task 4: Hyperpartisan News Detection

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    Published Nov 22, 2018
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Data for PAN at SemEval 2019 Task 4: Hyperpartisan News Detection

3 scholarly articles cite this dataset (View in Google Scholar)
  • Dataset published Nov 22, 2018
Dataset provided by
Bauhaus University, Weimarhttp://www.uni-weimar.de/
Leipzig Universityhttp://www.uni-leipzig.de/
Factmata Ltd.
Authors
Maria Mestre; Johannes Kiesel; Benno Stein; Martin Potthast; Rishabh Shukla; David Corney; Payam Adineh; Emmanuel Vincent
Description

Training and validation data for the PAN @ SemEval 2019 Task 4: Hyperpartisan News Detection.

The data is split into multiple files. The articles are contained in the files with names starting with "articles-" (which validate against the XML schema article.xsd). The ground-truth information is contained in the files with names starting with "ground-truth-" (which validate against the XML schema ground-truth.xsd).

The first part of the data (filename contains "bypublisher") is labeled by the overall bias of the publisher as provided by BuzzFeed journalists or MediaBiasFactCheck.com. It contains a total of 750,000 articles, half of which (375,000) are hyperpartisan and half of which are not. Half of the articles that are hyperpartisan (187,500) are on the left side of the political spectrum, half are on the right side. This data is split into a training set (80%, 600,000 articles) and a validation set (20%, 150,000 articles), where no publisher that occurs in the training set also occurs in the validation set. Similarly, none of the publishers in those sets will occur in the test set.

The second part of the data (filename contains "byarticle") is labeled through crowdsourcing on an article basis. The data contains only articles for which a consensus among the crowdsourcing workers existed. It contains a total of 645 articles. Of these, 238 (37%) are hyperpartisan and 407 (63%) are not, We will use a similar (but balanced!) test set. Again, none of the publishers in this set will occur in the test set.

Note that article IDs are only unique within the parts.

Acknowledgements: Thanks to Jonathan Miller for his assistance in cleaning the data!

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