The POLUSA Dataset : 0.9M Political News Articles Balanced by Time and Outlet Popularity

Lade...
Vorschaubild
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2020
Autor:innen
Gebhard, Lukas
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
Internationale Patentnummer
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published
Erschienen in
HUANG, Ruhua, ed. and others. Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020 (JCDL '20). New York: ACM, 2020, pp. 467-468. ISBN 978-1-4503-7585-6. Available under: doi: 10.1145/3383583.3398567
Zusammenfassung

News articles covering policy issues are an essential source of information in the social sciences and are also frequently used for other use cases, e.g., to train NLP language models. To derive meaningful insights from the analysis of news, large datasets are required that represent real-world distributions, e.g., with respect to the contained outlets' popularity, topically, or across time. Information on the political leanings of media publishers is often needed, e.g., to study differences in news reporting across the political spectrum, which is one of the prime use cases in the social sciences when studying media bias and related societal issues. Concerning these requirements, existing datasets have major flaws, resulting in redundant and cumbersome effort in the research community for dataset creation. To fill this gap, we present POLUSA, a dataset that represents the online media landscape as perceived by an average US news consumer. The dataset contains 0.9M articles covering policy topics published between Jan. 2017 and Aug. 2019 by 18 news outlets representing the political spectrum. Each outlet is labeled by its political leaning, which we derive using a systematic aggregation of eight data sources. The news dataset is balanced with respect to publication date and outlet popularity. POLUSA enables studying a variety of subjects, e.g., media effects and political partisanship. Due to its size, the dataset allows to utilize data-intense deep learning methods.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Konferenz
JCDL '20, 1. Aug. 2020 - 5. Aug. 2020, China (Virtual Event)
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690GEBHARD, Lukas, Felix HAMBORG, 2020. The POLUSA Dataset : 0.9M Political News Articles Balanced by Time and Outlet Popularity. JCDL '20. China (Virtual Event), 1. Aug. 2020 - 5. Aug. 2020. In: HUANG, Ruhua, ed. and others. Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020 (JCDL '20). New York: ACM, 2020, pp. 467-468. ISBN 978-1-4503-7585-6. Available under: doi: 10.1145/3383583.3398567
BibTex
@inproceedings{Gebhard2020-05-27T14:24:11ZPOLUS-51924,
  year={2020},
  doi={10.1145/3383583.3398567},
  title={The POLUSA Dataset : 0.9M Political News Articles Balanced by Time and Outlet Popularity},
  isbn={978-1-4503-7585-6},
  publisher={ACM},
  address={New York},
  booktitle={Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020 (JCDL '20)},
  pages={467--468},
  editor={Huang, Ruhua},
  author={Gebhard, Lukas and Hamborg, Felix}
}
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/123456789/51924">
    <dc:rights>terms-of-use</dc:rights>
    <dc:contributor>Gebhard, Lukas</dc:contributor>
    <dc:creator>Hamborg, Felix</dc:creator>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:title>The POLUSA Dataset : 0.9M Political News Articles Balanced by Time and Outlet Popularity</dcterms:title>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:abstract xml:lang="eng">News articles covering policy issues are an essential source of information in the social sciences and are also frequently used for other use cases, e.g., to train NLP language models. To derive meaningful insights from the analysis of news, large datasets are required that represent real-world distributions, e.g., with respect to the contained outlets' popularity, topically, or across time. Information on the political leanings of media publishers is often needed, e.g., to study differences in news reporting across the political spectrum, which is one of the prime use cases in the social sciences when studying media bias and related societal issues. Concerning these requirements, existing datasets have major flaws, resulting in redundant and cumbersome effort in the research community for dataset creation. To fill this gap, we present POLUSA, a dataset that represents the online media landscape as perceived by an average US news consumer. The dataset contains 0.9M articles covering policy topics published between Jan. 2017 and Aug. 2019 by 18 news outlets representing the political spectrum. Each outlet is labeled by its political leaning, which we derive using a systematic aggregation of eight data sources. The news dataset is balanced with respect to publication date and outlet popularity. POLUSA enables studying a variety of subjects, e.g., media effects and political partisanship. Due to its size, the dataset allows to utilize data-intense deep learning methods.</dcterms:abstract>
    <dc:contributor>Hamborg, Felix</dc:contributor>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-11-25T14:26:53Z</dcterms:available>
    <dcterms:issued>2020-05-27T14:24:11Z</dcterms:issued>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/51924"/>
    <dc:creator>Gebhard, Lukas</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-11-25T14:26:53Z</dc:date>
    <dc:language>eng</dc:language>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Nein
Begutachtet
Diese Publikation teilen