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2 datasets found
  1. Webis-Editorials-16

    • webis.de
    • zenodo.org
    Updated 2016
  2. BuzzFeed-Webis Fake News Corpus 16

    • webis.de
    1181813
    Updated Feb 20, 2018
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Al-Khatib, Khalid; Wachsmuth, Henning; Kiesel, Johannes; Hagen, Matthias; Stein, Benno; Göring, Steve (2016). Webis-Editorials-16 [Dataset]. http://doi.org/10.5281/zenodo.3254405
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Webis-Editorials-16

9 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
2016
Dataset provided by
Bauhaus-Universität Weimarhttp://www.uni-weimar.de/
The Web Technology & Information Systems Network
Authors
Al-Khatib, Khalid; Wachsmuth, Henning; Kiesel, Johannes; Hagen, Matthias; Stein, Benno; Göring, Steve
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

The Webis-Editorials-16 corpus is a novel corpus with 300 news editorials evenly selected from three diverse online news portals: Al Jazeera, Fox News, and The Guardian. The aim of the corpus is to study (1) the mining and classification of fine-grained types of argumentative discourse units and (2) the analysis of argumentation strategies pursued in editorials to achieve persuasion. To this end, each editorial contains manual type annotations of all units that capture the role that a unit plays in the argumentative discourse, such as assumption or statistics. The corpus consists of 14,313 units of six different types, each annotated by three professional annotators from the crowdsourcing platform upwork.com.

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