The individual dynamics of affective expression on social media
Dateien
Datum
Autor:innen
Herausgeber:innen
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
DOI (zitierfähiger Link)
Internationale Patentnummer
Link zur Lizenz
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Sammlungen
Core Facility der Universität Konstanz
Titel in einer weiteren Sprache
Publikationstyp
Publikationsstatus
Erschienen in
Zusammenfassung
Understanding the temporal dynamics of affect is crucial for our understanding human emotions in general. In this study, we empirically test a computational model of affective dynamics by analyzing a large-scale dataset of Facebook status updates using text analysis techniques. Our analyses support the central assumptions of our model: After stimulation, affective states, quantified as valence and arousal, exponentially return to an individual-specific baseline. On average, this baseline is at a slightly positive valence value and at a moderate arousal point below the midpoint. Furthermore, affective expression, in this case posting a status update on Facebook, immediately pushes arousal and valence towards the baseline by a proportional value. These results are robust to the choice of the text analysis technique and illustrate the fast timescale of affective dynamics through social media text. These outcomes are of high relevance for affective computing, the detection and modeling of collective emotions, the refinement of psychological research methodology, and the detection of abnormal, and potentially pathological, individual affect dynamics.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
Schlagwörter
Konferenz
Rezension
Zitieren
ISO 690
PELLERT, Max, Simon SCHWEIGHOFER, David GARCIA, 2020. The individual dynamics of affective expression on social media. In: EPJ Data Science. SpringerOpen. 2020, 9, 1. eISSN 2193-1127. Available under: doi: 10.1140/epjds/s13688-019-0219-3BibTex
@article{Pellert2020indiv-59782, year={2020}, doi={10.1140/epjds/s13688-019-0219-3}, title={The individual dynamics of affective expression on social media}, volume={9}, journal={EPJ Data Science}, author={Pellert, Max and Schweighofer, Simon and Garcia, David}, note={Article Number: 1} }
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/123456789/59782"> <dcterms:title>The individual dynamics of affective expression on social media</dcterms:title> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2023-01-18T12:19:47Z</dcterms:available> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/42"/> <dcterms:issued>2020</dcterms:issued> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/42"/> <dc:contributor>Garcia, David</dc:contributor> <dc:contributor>Pellert, Max</dc:contributor> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/59782/1/Pellert_2-1qf6ou3m3nygk5.pdf"/> <dc:creator>Garcia, David</dc:creator> <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by/4.0/"/> <dc:creator>Schweighofer, Simon</dc:creator> <dc:creator>Pellert, Max</dc:creator> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2023-01-18T12:19:47Z</dc:date> <dc:language>eng</dc:language> <dcterms:abstract xml:lang="eng">Understanding the temporal dynamics of affect is crucial for our understanding human emotions in general. In this study, we empirically test a computational model of affective dynamics by analyzing a large-scale dataset of Facebook status updates using text analysis techniques. Our analyses support the central assumptions of our model: After stimulation, affective states, quantified as valence and arousal, exponentially return to an individual-specific baseline. On average, this baseline is at a slightly positive valence value and at a moderate arousal point below the midpoint. Furthermore, affective expression, in this case posting a status update on Facebook, immediately pushes arousal and valence towards the baseline by a proportional value. These results are robust to the choice of the text analysis technique and illustrate the fast timescale of affective dynamics through social media text. These outcomes are of high relevance for affective computing, the detection and modeling of collective emotions, the refinement of psychological research methodology, and the detection of abnormal, and potentially pathological, individual affect dynamics.</dcterms:abstract> <foaf:homepage rdf:resource="http://localhost:8080/"/> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/59782/1/Pellert_2-1qf6ou3m3nygk5.pdf"/> <dc:contributor>Schweighofer, Simon</dc:contributor> <dc:rights>Attribution 4.0 International</dc:rights> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/59782"/> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> </rdf:Description> </rdf:RDF>