Social media data in affective science

Pellert, Max; Schweighofer, Simon; Garcia, David

Social media data in affective science

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2021

Autor:innen

Pellert, Max

Schweighofer, Simon

Garcia, David

Publikationstyp

Beitrag zu einem Sammelband

Publikationsstatus

Published

Erschienen in

Handbook of Computational Social Science, Volume 1 : Theory, Case Studies and Ethics / Engel, Uwe; Quan-Haase, Anabel; Liu, Sunny et al. (Hrsg.). - London : Routledge, 2021. - S. 240-255. - ISBN 978-0-367-45653-5

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.

Fachgebiet (DDC)

004 Informatik

Zitieren

ISO 690

PELLERT, 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, 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>