Datensatz:

Results and further resources concerning our pre-studies concerning revealing biases in news articles

Lade...
Vorschaubild

Datum der Erstveröffentlichung

September 20, 2021

Andere Beitragende

Repositorium der Erstveröffentlichung

Zenodo

Version des Datensatzes

Angaben zur Forschungsförderung

Projekt

Core Facility der Universität Konstanz
Bewerten Sie die FAIRness der Forschungsdaten

Gesperrt bis

Titel in einer weiteren Sprache

Publikationsstatus
Published

Zusammenfassung

Slanted news coverage strongly affects public opinion. This is especially true for coverage on politics and related issues, where studies have shown that bias in the news may strongly influence elections and other collective decisions. Due to its viable importance, news coverage has long been studied in the social sciences, resulting in comprehensive models to describe it and effective yet costly methods to analyze it, such as content analysis. We present an in-progress system for news recommendation that is the first to automate the manual procedure of content analysis to reveal person-targeting biases in news articles reporting on policy issues. In a large-scale user study, we find very promising results regarding this interdisciplinary research direction. Our recommender detects and reveals substantial frames that are actually present in individual news articles. In contrast, prior work rather only facilitates the visibility of biases, e.g., by distinguishing left- and right-wing outlets. Further, our study shows that recommending news articles that differently frame an event significantly improves respondents' awareness of bias.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
070 Nachrichtenmedien, Journalismus, Verlagswesen

Schlagwörter

media bias

Zugehörige Publikationen in KOPS

Link zu zugehöriger Publikation
Link zu zugehörigem Datensatz

Zitieren

ISO 690HAMBORG, Felix, Timo SPINDE, Kim HEINSER, Karsten DONNAY, Bela GIPP, 2021. Results and further resources concerning our pre-studies concerning revealing biases in news articles
BibTex
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/123456789/74225">
    <dcterms:title>Results and further resources concerning our pre-studies concerning revealing biases in news articles</dcterms:title>
    <dc:creator>Heinser, Kim</dc:creator>
    <dc:contributor>Donnay, Karsten</dc:contributor>
    <dc:creator>Gipp, Bela</dc:creator>
    <dc:creator>Donnay, Karsten</dc:creator>
    <dc:creator>Spinde, Timo</dc:creator>
    <dc:contributor>Gipp, Bela</dc:contributor>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:rights rdf:resource="https://creativecommons.org/licenses/by/3.0/de/legalcode"/>
    <dc:contributor>Hamborg, Felix</dc:contributor>
    <dcterms:issued>2021-09-20</dcterms:issued>
    <dcterms:created rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-09-20T12:28:50Z</dcterms:created>
    <dc:contributor>Heinser, Kim</dc:contributor>
    <dc:rights>Creative Commons Attribution 3.0 Germany</dc:rights>
    <dc:language>eng</dc:language>
    <dcterms:abstract>Slanted news coverage strongly affects public opinion. This is especially true for coverage on politics and related issues, where studies have shown that bias in the news may strongly influence elections and other collective decisions. Due to its viable importance, news coverage has long been studied in the social sciences, resulting in comprehensive models to describe it and effective yet costly methods to analyze it, such as content analysis. We present an in-progress system for news recommendation that is the first to automate the manual procedure of content analysis to reveal person-targeting biases in news articles reporting on policy issues. In a large-scale user study, we find very promising results regarding this interdisciplinary research direction. Our recommender detects and reveals substantial frames that are actually present in individual news articles. In contrast, prior work rather only facilitates the visibility of biases, e.g., by distinguishing left- and right-wing outlets. Further, our study shows that recommending news articles that differently frame an event significantly improves respondents' awareness of bias.</dcterms:abstract>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/71925"/>
    <dc:contributor>Spinde, Timo</dc:contributor>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2025-08-05T13:25:17Z</dc:date>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/74225"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2025-08-05T13:25:17Z</dcterms:available>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/71925"/>
    <dc:creator>Hamborg, Felix</dc:creator>
  </rdf:Description>
</rdf:RDF>
URL (Link zu den Daten)

Prüfdatum der URL

Kommentar zur Publikation

Universitätsbibliographie
Ja
Diese Publikation teilen