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  4. Claimspotting. On the Role and Automation of Fact-checking
 
Zitierlink DOI
10.26092/elib/4376

Claimspotting. On the Role and Automation of Fact-checking

Veröffentlichungsdatum
2025-02-24
Autoren
Nenno, Sami
Betreuer
Puschmann, Cornelius  
Humprecht, Edda
Gutachter
van der Woude, Judith  
Katzenbach, Christian  
Zusammenfassung
The present PhD framework text summarises six contributions that were written between 2022 and 2024, some of which have been published in peer-reviewed journals. The subject of the PhD thesis is claim detection, which is the automated monitoring of potential misinformation to assist fact-checkers. The two main research questions are how to design claim detection with respect to the needs of fact-checkers and the behaviour of social media users, and how to implement it with regard to technical limitations. Accordingly, the contributions are either empirical studies or methodological contributions. In particular, they include a quantitative analysis of fact-checks, interviews with fact-checkers, a computer-assisted analysis of flagged content on Facebook, the evaluation of existing claim detection datasets, the introduction of a new dataset for it, and the development of a domain specific named entity recognition model. The framework text provides an introduction to fact-checking in Germany, which is followed by an overview of the contributions. The main part introduces and answers the research questions; the remainder places claim detection in the overarching context of fact-checking and misinformation.
Schlagwörter
Fact-checking

; 

Disinformation

; 

Natural Language Processing
Institution
Universität Bremen  
Fachbereich
Fachbereich 09: Kulturwissenschaften (FB 09)  
Institute
Zentrum für Medien-, Kommunikations- und Informationsforschung (ZeMKI)  
Dokumenttyp
Dissertation
Lizenz
https://creativecommons.org/licenses/by/4.0/
Sprache
Englisch
Dateien
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Nenno_Claimspotting_Dissertation.pdf

Size

4.68 MB

Format

Adobe PDF

Checksum

(MD5):dc7aab4246b1a2b7209fc9ff34cd5110

Lade...
Vorschaubild
Name

Nenno_Claimspotting_Dissertation.pdf

Size

4.68 MB

Format

Adobe PDF

Checksum

(MD5):dc7aab4246b1a2b7209fc9ff34cd5110

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