3 datasets found
  1. W

    Webis-Editorials-16

    • webis.de
    • anthology.aicmu.ac.cn
    3254405
    Updated 2016
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    Steve Göring; Henning Wachsmuth; Johannes Kiesel; Matthias Hagen; Benno Stein (2016). Webis-Editorials-16 [Dataset]. http://doi.org/10.5281/zenodo.3254405
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    3254405Available download formats
    Dataset updated
    2016
    Dataset provided by
    Leibniz Universität Hannover
    Friedrich Schiller University Jena
    University of Groningen
    The Web Technology & Information Systems Network
    GESIS - Leibniz Institute for the Social Sciences
    Bauhaus-Universität Weimar
    Authors
    Steve Göring; Henning Wachsmuth; Johannes Kiesel; Matthias Hagen; Benno Stein
    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.

  2. E

    Webis-Editorials-16

    • live.european-language-grid.eu
    • data.niaid.nih.gov
    txt
    Updated Apr 30, 2024
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    (2024). Webis-Editorials-16 [Dataset]. https://live.european-language-grid.eu/catalogue/corpus/7538
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    txtAvailable download formats
    Dataset updated
    Apr 30, 2024
    License

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

    Description

    Newer Version of this corpus in the 2018 version can be found here: https://doi.org/10.5281/zenodo.1340629

    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.

  3. W

    BuzzFeed-Webis Fake News Corpus 16

    • webis.de
    • paperswithcode.com
    • +2more
    1181813
    Updated 2018
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    Martin Potthast; Johannes Kiesel; Kevin Reinartz; Janek Bevendorff; Benno Stein (2018). BuzzFeed-Webis Fake News Corpus 16 [Dataset]. http://doi.org/10.5281/zenodo.1181813
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    1181813Available download formats
    Dataset updated
    2018
    Dataset provided by
    Bauhaus-Universität Weimar and Leipzig University
    The Web Technology & Information Systems Network
    University of Kassel, hessian.AI, and ScaDS.AI
    GESIS - Leibniz Institute for the Social Sciences
    Bauhaus-Universität Weimar
    Authors
    Martin Potthast; Johannes Kiesel; Kevin Reinartz; Janek Bevendorff; Benno Stein
    License

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

    Description

    The BuzzFeed-Webis Fake News Corpus 16 comprises the output of 9 publishers in a week close to the US elections. Among the selected publishers are 6 prolific hyperpartisan ones (three left-wing and three right-wing), and three mainstream publishers (see Table 1). All publishers earned Facebook’s blue checkmark, indicating authenticity and an elevated status within the network. For seven weekdays (September 19 to 23 and September 26 and 27), every post and linked news article of the 9 publishers was fact-checked by professional journalists at BuzzFeed. In total, 1,627 articles were checked, 826 mainstream, 256 left-wing and 545 right-wing. The imbalance between categories results from differing publication frequencies.

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Share
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TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Steve Göring; Henning Wachsmuth; Johannes Kiesel; Matthias Hagen; Benno Stein (2016). Webis-Editorials-16 [Dataset]. http://doi.org/10.5281/zenodo.3254405

Webis-Editorials-16

Explore at:
14 scholarly articles cite this dataset (View in Google Scholar)
3254405Available download formats
Dataset updated
2016
Dataset provided by
Leibniz Universität Hannover
Friedrich Schiller University Jena
University of Groningen
The Web Technology & Information Systems Network
GESIS - Leibniz Institute for the Social Sciences
Bauhaus-Universität Weimar
Authors
Steve Göring; Henning Wachsmuth; Johannes Kiesel; Matthias Hagen; Benno Stein
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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