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  <doc>
    <id>3640</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>404</pageFirst>
    <pageLast>421</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace>Bologna</publisherPlace>
    <creatingCorporation>University of Bologna</creatingCorporation>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Overview of the CLEF-2022 CheckThat! Lab: Task 3 on Fake News Detection</title>
    <abstract language="eng">This paper describes the results of the CheckThat! Lab 2022 Task 3. This is the fifth edition of the lab, which concentrates on the evaluation of technologies supporting three tasks related to factuality. Task 3 is designed as a multi-class classification problem and focuses on the veracity of German and English news articles. The German subtask is ought to be solved using an cross-lingual approach while the English subtask was offered as mono-lingual task. The participants of the lab were provided an English training, development and test dataset as well as a German test dataset. In total, 25 teams submitted successful runs for the English subtask and 8 for the German subtask. The best performing system for the mono-lingual subtask achieved a macro F1-score of 0.339. The best system for the cross-lingual task achieved a macro F1-score of 0.242. In the paper at hand we will elaborate on the process of data collection, the task setup, the evaluation results and give a brief overview of the participating systems.</abstract>
    <parentTitle language="eng">CLEF 2022 Working Notes : Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum</parentTitle>
    <identifier type="issn">1613-0073</identifier>
    <identifier type="urn">urn:nbn:de:0074-3180-7</identifier>
    <identifier type="url">https://ceur-ws.org/Vol-3180/paper-30.pdf</identifier>
    <enrichment key="opus.import.date">2025-04-07T10:37:15+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">hisres</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Juliane Köhler</author>
    <author>Gautam Kishore Shahi</author>
    <author>Julia Maria Struß</author>
    <author>Michael Wiegand</author>
    <author>Melanie Siegel</author>
    <author>Thomas Mandl</author>
    <author>Mina Schütz</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Deep Learning</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Desinformation</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fehlinformation</value>
    </subject>
    <collection role="ddc" number="000">Informatik, Wissen, Systeme</collection>
    <collection role="institutes" number="fb5">FB5 Informationswissenschaften</collection>
    <collection role="institutes" number="fb5b">Publikationen des FB Informationswissenschaften</collection>
    <collection role="Import" number="import">Import</collection>
    <collection role="open_access_fhp" number="">Gold Open Access</collection>
    <thesisPublisher>Fachhochschule Potsdam</thesisPublisher>
  </doc>
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