Quantifying Search Bias : Investigating Sources of Bias for Political Searches in Social Media

Lade...
Vorschaubild
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2017
Autor:innen
Eslami, Motahhare
Messias, Johnnatan
Zafar, Muhammad Bilal
Ghosh, Saptarshi
Gummadi, Krishna P.
Karahalios, Karrie
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
Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published
Erschienen in
LEE, Charlotte P., ed. and others. CSCW '17 : Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. New York, NY: ACM, 2017, pp. 417-432. ISBN 978-1-4503-4335-0. Available under: doi: 10.1145/2998181.2998321
Zusammenfassung

Search systems in online social media sites are frequently used to find information about ongoing events and people. For topics with multiple competing perspectives, such as political events or political candidates, bias in the top ranked results significantly shapes public opinion. However, bias does not emerge from an algorithm alone. It is important to distinguish between the bias that arises from the data that serves as the input to the ranking system and the bias that arises from the ranking system itself. In this paper, we propose a framework to quantify these distinct biases and apply this framework to politics-related queries on Twitter. We found that both the input data and the ranking system contribute significantly to produce varying amounts of bias in the search results and in different ways. We discuss the consequences of these biases and possible mechanisms to signal this bias in social media search systems' interfaces.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Konferenz
CSCW '17: 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, 25. Feb. 2017 - 1. März 2017, Portland, Oregon
Rezension
undefined / . - undefined, undefined
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Datensätze
Zitieren
ISO 690KULSHRESTHA, Juhi, Motahhare ESLAMI, Johnnatan MESSIAS, Muhammad Bilal ZAFAR, Saptarshi GHOSH, Krishna P. GUMMADI, Karrie KARAHALIOS, 2017. Quantifying Search Bias : Investigating Sources of Bias for Political Searches in Social Media. CSCW '17: 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. Portland, Oregon, 25. Feb. 2017 - 1. März 2017. In: LEE, Charlotte P., ed. and others. CSCW '17 : Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. New York, NY: ACM, 2017, pp. 417-432. ISBN 978-1-4503-4335-0. Available under: doi: 10.1145/2998181.2998321
BibTex
@inproceedings{Kulshrestha2017-04-05T10:12:56ZQuant-53945,
  year={2017},
  doi={10.1145/2998181.2998321},
  title={Quantifying Search Bias : Investigating Sources of Bias for Political Searches in Social Media},
  isbn={978-1-4503-4335-0},
  publisher={ACM},
  address={New York, NY},
  booktitle={CSCW '17 : Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing},
  pages={417--432},
  editor={Lee, Charlotte P.},
  author={Kulshrestha, Juhi and Eslami, Motahhare and Messias, Johnnatan and Zafar, Muhammad Bilal and Ghosh, Saptarshi and Gummadi, Krishna P. and Karahalios, Karrie}
}
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/123456789/53945">
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-06-10T12:29:19Z</dcterms:available>
    <dc:creator>Ghosh, Saptarshi</dc:creator>
    <dc:contributor>Gummadi, Krishna P.</dc:contributor>
    <dcterms:issued>2017-04-05T10:12:56Z</dcterms:issued>
    <dc:rights>terms-of-use</dc:rights>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:contributor>Karahalios, Karrie</dc:contributor>
    <dc:contributor>Zafar, Muhammad Bilal</dc:contributor>
    <dc:language>eng</dc:language>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:contributor>Messias, Johnnatan</dc:contributor>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/53945"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/42"/>
    <dcterms:abstract xml:lang="eng">Search systems in online social media sites are frequently used to find information about ongoing events and people. For topics with multiple competing perspectives, such as political events or political candidates, bias in the top ranked results significantly shapes public opinion. However, bias does not emerge from an algorithm alone. It is important to distinguish between the bias that arises from the data that serves as the input to the ranking system and the bias that arises from the ranking system itself. In this paper, we propose a framework to quantify these distinct biases and apply this framework to politics-related queries on Twitter. We found that both the input data and the ranking system contribute significantly to produce varying amounts of bias in the search results and in different ways. We discuss the consequences of these biases and possible mechanisms to signal this bias in social media search systems' interfaces.</dcterms:abstract>
    <dc:contributor>Eslami, Motahhare</dc:contributor>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:creator>Messias, Johnnatan</dc:creator>
    <dc:contributor>Kulshrestha, Juhi</dc:contributor>
    <dc:contributor>Ghosh, Saptarshi</dc:contributor>
    <dcterms:title>Quantifying Search Bias : Investigating Sources of Bias for Political Searches in Social Media</dcterms:title>
    <dc:creator>Kulshrestha, Juhi</dc:creator>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/42"/>
    <dc:creator>Zafar, Muhammad Bilal</dc:creator>
    <dc:creator>Eslami, Motahhare</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-06-10T12:29:19Z</dc:date>
    <dc:creator>Karahalios, Karrie</dc:creator>
    <dc:creator>Gummadi, Krishna P.</dc:creator>
  </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