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Der Beitrag stellt Ergebnisse der Fachgruppe Informationskompetenz der KIBA vor, in der alle Lehrenden im Bereich der Vermittlung von Medien- und Informationskompetenz an bibliotheks- und informationswissenschaftlichen Studiengängen in Deutschland zusammenarbeiten. Ausgangspunkt ist das „Framework Informationskompetenz“, ein Anforderungsrahmen, der gemeinsame Standards in der Qualifikation von Studierenden der Bibliotheks- und Informationswissenschaft für das Aufgabenfeld der Förderung von Informationskompetenz sichern soll. Es wird aufgezeigt, wie die in diesem Rahmenmodell formulierten Qualifikationsstandards in den verschiedenen Studiengängen umgesetzt werden und wo es bedarfsbezogene Ausprägung und Gewichtung in den Qualifikationszielen gibt.
Overview of the CLEF-2022 CheckThat! Lab on Fighting the COVID-19 Infodemic and Fake News Detection
(2022)
We describe the fifth edition of the CheckThat! lab, part of the 2022 Conference and Labs of the Evaluation Forum (CLEF). The lab evaluates technology supporting tasks related to factuality in multiple languages: Arabic, Bulgarian, Dutch, English, German, Spanish, and Turkish. Task 1 asks to identify relevant claims in tweets in terms of check-worthiness, verifiability, harmfullness, and attention-worthiness. Task 2 asks to detect previously fact-checked claims that could be relevant to fact-check a new claim. It targets both tweets and political debates/speeches. Task 3 asks to predict the veracity of the main claim in a news article. CheckThat! was the most popular lab at CLEF-2022 in terms of team registrations: 137 teams. More than one-third (37%) of them actually participated: 18, 7, and 26 teams submitted 210, 37, and 126 official runs for tasks 1, 2, and 3, respectively.
The fifth edition of the CheckThat! Lab is held as part of the 2022 Conference and Labs of the Evaluation Forum (CLEF). The lab evaluates technology supporting various factuality tasks in seven languages: Arabic, Bulgarian, Dutch, English, German, Spanish, and Turkish. Task 1 focuses on disinformation related to the ongoing COVID-19 infodemic and politics, and asks to predict whether a tweet is worth fact-checking, contains a verifiable factual claim, is harmful to the society, or is of interest to policy makers and why. Task 2 asks to retrieve claims that have been previously fact-checked and that could be useful to verify the claim in a tweet. Task 3 is to predict the veracity of a news article. Tasks 1 and 3 are classification problems, while Task 2 is a ranking one.