Detection and Identification of Fake News: Binary Content Classification with Pre-trained Language Models

URN to cite this document:: urn:nbn:de:bvb:355-epub-449597
DOI to cite this document:: 10.5283/epub.44959

Schütz, Mina

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Date of publication of this fulltext: 18 Apr 2021 13:29

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Item type:	Book section
ISBN:	978-3-86488-172-5
Open Access Type:	Individuel Contract
Title of Book:	Information between Data and Knowledge
Publisher:	Werner Hülsbusch
Place of Publication:	Glückstadt
Other Series:	Schriften zur Informationswissenschaft
Volume:	74
Page Range:	pp. 422-431
Date:	2021
Additional Information (public):	Gerhard Lustig Award Papers
Institutions:	Languages and Literatures > Institut für Information und Medien, Sprache und Kultur (I:IMSK) > Lehrstuhl für Medieninformatik (Prof. Dr. Christian Wolff) Informatics and Data Science > Department Human-Centered Computing > Lehrstuhl für Medieninformatik (Prof. Dr. Christian Wolff)
Keywords:	fake news; fake news detection; BERT; transformer; pre-trained language model; binary classification
Dewey Decimal Classification:	000 Computer science, information & general works > 020 Library & information sciences
Status:	Published
Refereed:	Yes, this version has been refereed
Created at the University of Regensburg:	No
Item ID:	44959

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Abstract

Fake news has emerged as a critical problem for society and professional journalism. Many individuals consume their news via online media, such as social networks and news websites. Therefore, the demand for automatic fake news detection is increasing. There is still no agreed upon definition for fake news, since it can include various concepts, such as clickbait, propaganda, satire, hoaxes, and ...

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