2 datasets found
  1. W

    Webis-Clickbait-22

    • webis.de
    • anthology.aicmu.ac.cn
    Updated 2022
    + more versions
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    Matthias Hagen; Maik Fröbe; Artur Jurk; Martin Potthast (2022). Webis-Clickbait-22 [Dataset]. https://webis.de/data/webis-clickbait-22.html
    Explore at:
    Dataset updated
    2022
    Dataset provided by
    Friedrich Schiller University Jena
    University of Kassel, hessian.AI, and ScaDS.AI
    The Web Technology & Information Systems Network
    Authors
    Matthias Hagen; Maik Fröbe; Artur Jurk; Martin Potthast
    License

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

    Description

    The Webis Clickbait Spoiling Corpus 2022 (Webis-Clickbait-22) contains 5,000 spoiled clickbait posts crawled from Facebook, Reddit, and Twitter. This corpus supports the task of clickbait spoiling, which deals with generating a short text that satisfies the curiosity induced by a clickbait post.

  2. o

    Webis Clickbait Spoiling Corpus 2022

    • explore.openaire.eu
    Updated Mar 16, 2022
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    Matthias Hagen; Maik Fröbe; Artur Jurk; Martin Potthast (2022). Webis Clickbait Spoiling Corpus 2022 [Dataset]. http://doi.org/10.5281/zenodo.6362725
    Explore at:
    Dataset updated
    Mar 16, 2022
    Authors
    Matthias Hagen; Maik Fröbe; Artur Jurk; Martin Potthast
    Description

    Webis Clickbait Spoiling Corpus 2022 The Webis Clickbait Spoiling Corpus 2022 (Webis-Clickbait-22) contains 5,000 spoiled clickbait posts crawled from Facebook, Reddit, and Twitter. This corpus supports the task of clickbait spoiling, which deals with generating a short text that satisfies the curiosity induced by a clickbait post. This dataset contains the clickbait posts and manually cleaned versions of the linked documents, and extracted spoilers for each clickbait post. Additionally, the spoilers are categorized into three types: short phrase spoilers, longer passage spoilers, and multiple non-consecutive pieces of text. This dataset contains the clickbait posts and manually cleaned versions of the linked documents, and extracted spoilers for each clickbait post. Additionally, the spoilers are categorized into three types: short phrase spoilers, longer passage spoilers, and multiple non-consecutive pieces of text. The test set of this dataset was used for the SemEval-2023 clickbait spoiling task. You can re-execute and adopt the software submissions made through for this SemEval task, please see the instructions and overview of approaches in TIRA. Overview The dataset comes with predefined train/validation/test splits: training.jsonl: 3,200 posts for training validation.jsonl: 800 posts for validation test.jsonl: 1,000 posts for testing The test set was used for the SemEval-2023 clickbait spoiling task. This shared task was organized with TIRA.io and participants submitted Docker software during the task. Please see the instructions in TIRA to re-execute or modify the approaches.

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Share
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TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Matthias Hagen; Maik Fröbe; Artur Jurk; Martin Potthast (2022). Webis-Clickbait-22 [Dataset]. https://webis.de/data/webis-clickbait-22.html

Webis-Clickbait-22

Explore at:
3 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
2022
Dataset provided by
Friedrich Schiller University Jena
University of Kassel, hessian.AI, and ScaDS.AI
The Web Technology & Information Systems Network
Authors
Matthias Hagen; Maik Fröbe; Artur Jurk; Martin Potthast
License

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

Description

The Webis Clickbait Spoiling Corpus 2022 (Webis-Clickbait-22) contains 5,000 spoiled clickbait posts crawled from Facebook, Reddit, and Twitter. This corpus supports the task of clickbait spoiling, which deals with generating a short text that satisfies the curiosity induced by a clickbait post.

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