Social media data in affective science

Lade...
Vorschaubild
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2021
Autor:innen
Pellert, Max
Schweighofer, Simon
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
ArXiv-ID
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 Sammelband
Publikationsstatus
Published
Erschienen in
ENGEL, Uwe, ed., Anabel QUAN-HAASE, ed., Sunny LIU, ed. and others. Handbook of Computational Social Science, Volume 1 : Theory, Case Studies and Ethics. London: Routledge, 2021, pp. 240-255. ISBN 978-0-367-45653-5. Available under: doi: 10.4324/9781003024583-18
Zusammenfassung

The digital traces generated by social media offer the opportunity to analyze human behavior at new scales, depths, and resolutions. The results of analyses of social media data, while sometimes difficult to generalize to a society as a whole, can give important insights on detailed actions and subjective states of individuals. This novel datasource offers a new window to tackle research questions from affective science with respect to emotion dynamics, collective emotions, and affective expression in social contexts. In this chapter, we present a balanced view of the benefits, risks, opportunities, and pitfalls of analyzing affective life through social media data. We review a variety of methods to quantify emotions and other affective states from social media data. We illustrate the application of these methods at new scales and resolutions in a series of examples from previous research. We present research gaps and open questions about the role, meaning, and functionality of affective expression in social media, pointing to emerging research trends in computational social science and social psychology. When used critically and with robust research methods, observational analyses of large-scale social media data can be complementary to traditional methodologies in psychology and cognitive science.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Konferenz
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690PELLERT, Max, Simon SCHWEIGHOFER, David GARCIA, 2021. Social media data in affective science. In: ENGEL, Uwe, ed., Anabel QUAN-HAASE, ed., Sunny LIU, ed. and others. Handbook of Computational Social Science, Volume 1 : Theory, Case Studies and Ethics. London: Routledge, 2021, pp. 240-255. ISBN 978-0-367-45653-5. Available under: doi: 10.4324/9781003024583-18
BibTex
@incollection{Pellert2021Socia-66309,
  year={2021},
  doi={10.4324/9781003024583-18},
  title={Social media data in affective science},
  isbn={978-0-367-45653-5},
  publisher={Routledge},
  address={London},
  booktitle={Handbook of Computational Social Science, Volume 1 : Theory, Case Studies and Ethics},
  pages={240--255},
  editor={Engel, Uwe and Quan-Haase, Anabel and Liu, Sunny},
  author={Pellert, Max and Schweighofer, Simon and Garcia, David}
}
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/123456789/66309">
    <dc:contributor>Schweighofer, Simon</dc:contributor>
    <dcterms:abstract xml:lang="eng">The digital traces generated by social media offer the opportunity to analyze human behavior at new scales, depths, and resolutions. The results of analyses of social media data, while sometimes difficult to generalize to a society as a whole, can give important insights on detailed actions and subjective states of individuals. This novel datasource offers a new window to tackle research questions from affective science with respect to emotion dynamics, collective emotions, and affective expression in social contexts. In this chapter, we present a balanced view of the benefits, risks, opportunities, and pitfalls of analyzing affective life through social media data. We review a variety of methods to quantify emotions and other affective states from social media data. We illustrate the application of these methods at new scales and resolutions in a series of examples from previous research. We present research gaps and open questions about the role, meaning, and functionality of affective expression in social media, pointing to emerging research trends in computational social science and social psychology. When used critically and with robust research methods, observational analyses of large-scale social media data can be complementary to traditional methodologies in psychology and cognitive science.</dcterms:abstract>
    <dc:creator>Schweighofer, Simon</dc:creator>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/66309"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:creator>Garcia, David</dc:creator>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/42"/>
    <dc:contributor>Garcia, David</dc:contributor>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:creator>Pellert, Max</dc:creator>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/42"/>
    <dc:rights>terms-of-use</dc:rights>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2023-03-06T15:12:28Z</dc:date>
    <dc:language>eng</dc:language>
    <dc:contributor>Pellert, Max</dc:contributor>
    <dcterms:title>Social media data in affective science</dcterms:title>
    <dcterms:issued>2021</dcterms:issued>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2023-03-06T15:12:28Z</dcterms:available>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
  </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