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  <doc>
    <id>3623</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>287</pageFirst>
    <pageLast>298</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace>Frankreich</publisherPlace>
    <creatingCorporation>University of Grenoble Alpes</creatingCorporation>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
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    <title language="eng">Overview of the CLEF-2024 CheckThat! Lab Task 2 on Subjectivity in News Articles</title>
    <abstract language="eng">We present an overview of Task 2 of the seventh edition of the CheckThat! lab at the 2024 iteration of the Conference and Labs of the Evaluation Forum (CLEF). The task focuses on subjectivity detection in news articles and was o ered in five languages: Arabic, Bulgarian, English, German, and Italian, as well as in a multilingual setting. The datasets for each language were carefully curated and annotated, comprising over 10,000 sentences from news articles. The task challenged participants to develop systems capable of distinguishing between subjective statements (refecting personal opinions or biases) and objective ones (presenting factual information) at the sentence level. A total of 15 teams participated in the task, submitting 36 valid runs across all language tracks. The participants used a variety of approaches, with transformer-based models being the most popular choice. Strategies included fine-tuning monolingual and multilingual models, and leveraging English models with automatic translation for the non-English datasets. Some teams also explored ensembles, feature engineering, and innovative techniques such as few-shot learning and in-context learning with large language models. The evaluation was based on macro-averaged F1 score. The results varied across languages, with the best performance achieved for Italian and German, followed by English. The Arabic track proved particularly challenging, with no team surpassing an F1 score of 0.50. This task contributes to the broader goal of enhancing the reliability of automated content analysis in the context of misinformation detection and fact-checking. The paper provides detailed insights into the datasets, participant approaches, and results, o ering a benchmark for the current state of subjectivity detection across multiple languages.</abstract>
    <parentTitle language="eng">CLEF 2024 Working Notes : Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2024)</parentTitle>
    <subTitle language="eng">Notebook for the CheckThat! Lab at CLEF 2024</subTitle>
    <identifier type="issn">1613-0073</identifier>
    <identifier type="urn">urn:nbn:de:0074-3740-3</identifier>
    <identifier type="url">https://ceur-ws.org/Vol-3740/paper-25.pdf</identifier>
    <enrichment key="opus.import.date">2025-03-26T15:59:16+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>Julia Maria Struß</author>
    <author>Federico Ruggeri</author>
    <author>Alberto Barrón-Cedeño</author>
    <author>Firoj Alam</author>
    <author>Dimitar Dimitrov</author>
    <author>Andrea Galassi</author>
    <author>Georgi Pachov</author>
    <author>Ivan Koychev</author>
    <author>Preslav Nakov</author>
    <author>Melanie Siegel</author>
    <author>Michael Wiegand</author>
    <author>Maram Hasanain</author>
    <author>Reem Suwaileh</author>
    <author>Wajdi Zaghouani</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fehlinformation</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Zeitungsartikel</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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