Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/9011
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dc.contributor.authorNakov P.-
dc.contributor.authorBarrón-Cedeño A.-
dc.contributor.authorDa San Martino G.-
dc.contributor.authorAlam F.-
dc.contributor.authorMíguez R.-
dc.contributor.authorCaselli T.-
dc.contributor.authorKartal Y.S.-
dc.date.accessioned2022-11-30T19:26:28Z-
dc.date.available2022-11-30T19:26:28Z-
dc.date.issued2022-
dc.identifier.issn1613-0073-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/9011-
dc.description2022 Conference and Labs of the Evaluation Forum, CLEF 2022 -- 5 September 2022 through 8 September 2022 -- -- 181762en_US
dc.description.abstractWe present an overview of CheckThat! lab 2022 Task 1, part of the 2022 Conference and Labs of the Evaluation Forum (CLEF). Task 1 asked to predict which posts in a Twitter stream are worth fact-checking, focusing on COVID-19 and politics in six languages: Arabic, Bulgarian, Dutch, English, Spanish, and Turkish. A total of 19 teams participated and most submissions managed to achieve sizable improvements over the baselines using Transformer-based models such as BERT and GPT-3. Across the four subtasks, approaches that targetted multiple languages (be it individually or in conjunction, in general obtained the best performance. We describe the dataset and the task setup, including the evaluation settings, and we give a brief overview of the participating systems. As usual in the CheckThat! lab, we release to the research community all datasets from the lab as well as the evaluation scripts, which should enable further research on finding relevant tweets that can help different stakeholders such as fact-checkers, journalists, and policymakers. © 2022 Copyright for this paper by its authors.en_US
dc.description.sponsorshipHamad Bin Khalifa University, HBKUen_US
dc.description.sponsorshipPart of this work is made within the Tanbih mega-project, developed at the Qatar Computing Research Institute, HBKU, which aims to limit the impact of “fake news”, propaganda, and media bias by making users aware of what they are reading, thus promoting media literacy and critical thinking.en_US
dc.language.isoenen_US
dc.publisherCEUR-WSen_US
dc.relation.ispartofCEUR Workshop Proceedingsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCheck-Worthiness Estimationen_US
dc.subjectComputational Journalismen_US
dc.subjectCOVID-19en_US
dc.subjectFact-Checkingen_US
dc.subjectSocial Media Verificationen_US
dc.subjectVeracityen_US
dc.subjectLaboratoriesen_US
dc.subjectSocial networking (online)en_US
dc.subjectCheck-worthiness estimationen_US
dc.subjectComputational journalismen_US
dc.subjectFact-checkingen_US
dc.subjectMultiple languagesen_US
dc.subjectPerformanceen_US
dc.subjectSocial mediaen_US
dc.subjectSocial medium verificationen_US
dc.subjectSubtasken_US
dc.subjectTurkishsen_US
dc.subjectVeracityen_US
dc.subjectCOVID-19en_US
dc.titleOverview of the Clef-2022 Checkthat! Lab Task 1 on Identifying Relevant Claims in Tweetsen_US
dc.typeConference Objecten_US
dc.identifier.volume3180en_US
dc.identifier.startpage368en_US
dc.identifier.endpage392en_US
dc.identifier.scopus2-s2.0-85136928741en_US
dc.institutionauthorKutlu, Mücahid-
dc.authorscopusid15043153900-
dc.authorscopusid26321398000-
dc.authorscopusid55915092700-
dc.authorscopusid56024506200-
dc.authorscopusid57314168500-
dc.authorscopusid35932126700-
dc.authorscopusid35299304300-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopusquality--
dc.ozel2022v3_Editen_US
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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