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

No Thumbnail Available
Files
There are no files associated with this item.
Date
2017
Authors
Eslami, Motahhare
Messias, Johnnatan
Zafar, Muhammad Bilal
Ghosh, Saptarshi
Gummadi, Krishna P.
Karahalios, Karrie
Editors
Contact
Journal ISSN
Electronic ISSN
ISBN
Bibliographical data
Publisher
Series
URI (citable link)
DOI (citable link)
ArXiv-ID
International patent number
Link to the license
EU project number
Project
Open Access publication
Restricted until
Title in another language
Research Projects
Organizational Units
Journal Issue
Publication type
Contribution to a conference collection
Publication status
Published
Published in
CSCW '17 : Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing / Lee, Charlotte P. et al. (ed.). - New York, NY : ACM, 2017. - pp. 417-432. - ISBN 978-1-4503-4335-0
Abstract
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.
Summary in another language
Subject (DDC)
004 Computer Science
Keywords
Conference
CSCW '17: 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, Feb 25, 2017 - Mar 1, 2017, Portland, Oregon
Review
undefined / . - undefined, undefined. - (undefined; undefined)
Cite This
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, Feb 25, 2017 - Mar 1, 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, 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>
Internal note
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Contact
URL of original publication
Test date of URL
Examination date of dissertation
Method of financing
Comment on publication
Alliance license
Corresponding Authors der Uni Konstanz vorhanden
International Co-Authors
Bibliography of Konstanz
No
Refereed