Newsalyze : Enabling News Consumers to Understand Media Bias

Hamborg, Felix; Zhukova, Anastasia; Donnay, Karsten; Gipp, Bela

Newsalyze : Enabling News Consumers to Understand Media Bias

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2020

Publikationstyp

Beitrag zu einem Konferenzband

Publikationsstatus

Published

Erschienen in

JCDL '20 : Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020. New York, NY: ACM, 2020, pp. 455-456. ISBN 978-1-4503-7585-6. Available under: doi: 10.1145/3383583.3398561

Zusammenfassung

News is a central source of information for individuals to inform themselves on current topics. Knowing a news article's slant and authenticity is of crucial importance in times of "fake news," news bots, and centralization of media ownership. We introduce Newsalyze, a bias-aware news reader focusing on a subtle, yet powerful form of media bias, named bias by word choice and labeling (WCL). WCL bias can alter the assessment of entities reported in the news, e.g., "freedom fighters" vs. "terrorists." At the core of the analysis is a neural model that uses a news-adapted BERT language model to determine target-dependent sentiment, a high-level effect of WCL bias. While the analysis currently focuses on only this form of bias, the visualizations already reveal patterns of bias when contrasting articles (overview) and in-text instances of bias (article view).

Fachgebiet (DDC)

320 Politik

Konferenz

JCDL '20 : ACM/IEEE Joint Conference on Digital Libraries in 2020 (Virtual Event), 1. Aug. 2020 - 5. Aug. 2020, Wuhan, China

Zitieren

ISO 690

HAMBORG, Felix, Anastasia ZHUKOVA, Karsten DONNAY, Bela GIPP, 2020. Newsalyze : Enabling News Consumers to Understand Media Bias. JCDL '20 : ACM/IEEE Joint Conference on Digital Libraries in 2020 (Virtual Event). Wuhan, China, 1. Aug. 2020 - 5. Aug. 2020. In: JCDL '20 : Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020. New York, NY: ACM, 2020, pp. 455-456. ISBN 978-1-4503-7585-6. Available under: doi: 10.1145/3383583.3398561

BibTex

@inproceedings{Hamborg2020Newsa-51335,
  year={2020},
  doi={10.1145/3383583.3398561},
  title={Newsalyze : Enabling News Consumers to Understand Media Bias},
  isbn={978-1-4503-7585-6},
  publisher={ACM},
  address={New York, NY},
  booktitle={JCDL '20 : Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020},
  pages={455--456},
  author={Hamborg, Felix and Zhukova, Anastasia and Donnay, Karsten and Gipp, Bela}
}

RDF

<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/51335">
    <dc:creator>Donnay, Karsten</dc:creator>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Zhukova, Anastasia</dc:creator>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43613"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/51335"/>
    <dc:rights>terms-of-use</dc:rights>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/42"/>
    <dc:contributor>Donnay, Karsten</dc:contributor>
    <dcterms:issued>2020</dcterms:issued>
    <dc:contributor>Zhukova, Anastasia</dc:contributor>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-10-13T13:35:14Z</dc:date>
    <dcterms:title>Newsalyze : Enabling News Consumers to Understand Media Bias</dcterms:title>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43613"/>
    <dc:language>eng</dc:language>
    <dc:contributor>Hamborg, Felix</dc:contributor>
    <dcterms:abstract xml:lang="eng">News is a central source of information for individuals to inform themselves on current topics. Knowing a news article's slant and authenticity is of crucial importance in times of "fake news," news bots, and centralization of media ownership. We introduce Newsalyze, a bias-aware news reader focusing on a subtle, yet powerful form of media bias, named bias by word choice and labeling (WCL). WCL bias can alter the assessment of entities reported in the news, e.g., "freedom fighters" vs. "terrorists." At the core of the analysis is a neural model that uses a news-adapted BERT language model to determine target-dependent sentiment, a high-level effect of WCL bias. While the analysis currently focuses on only this form of bias, the visualizations already reveal patterns of bias when contrasting articles (overview) and in-text instances of bias (article view).</dcterms:abstract>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-10-13T13:35:14Z</dcterms:available>
    <dc:contributor>Gipp, Bela</dc:contributor>
    <dc:creator>Gipp, Bela</dc:creator>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/42"/>
    <dc:creator>Hamborg, Felix</dc:creator>
  </rdf:Description>
</rdf:RDF>

Universitätsbibliographie

Ja