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[journal article]

dc.contributor.authorBatzdorfer, Veronikade
dc.contributor.authorSteinmetz, Holgerde
dc.contributor.authorBiella, Marcode
dc.contributor.authorAlizadeh, Meysamde
dc.date.accessioned2023-08-16T12:19:47Z
dc.date.available2023-08-16T12:19:47Z
dc.date.issued2022de
dc.identifier.issn2364-4168de
dc.identifier.urihttps://www.ssoar.info/ssoar/handle/document/88564
dc.description.abstractThe COVID-19 pandemic resulted in an upsurge in the spread of diverse conspiracy theories (CTs) with real-life impact. However, the dynamics of user engagement remain under-researched. In the present study, we leverage Twitter data across 11 months in 2020 from the timelines of 109 CT posters and a comparison group (non-CT group) of equal size. Within this approach, we used word embeddings to distinguish non-CT content from CT-related content as well as analysed which element of CT content emerged in the pandemic. Subsequently, we applied time series analyses on the aggregate and individual level to investigate whether there is a difference between CT posters and non-CT posters in non-CT tweets as well as the temporal dynamics of CT tweets. In this regard, we provide a description of the aggregate and individual series, conducted a STL decomposition in trends, seasons, and errors, as well as an autocorrelation analysis, and applied generalised additive mixed models to analyse nonlinear trends and their differences across users. The narrative motifs, characterised by word embeddings, address pandemic-specific motifs alongside broader motifs and can be related to several psychological needs (epistemic, existential, or social). Overall, the comparison of the CT group and non-CT group showed a substantially higher level of overall COVID-19-related tweets in the non-CT group and higher level of random fluctuations. Focussing on conspiracy tweets, we found a slight positive trend but, more importantly, an increase in users in 2020. Moreover, the aggregate series of CT content revealed two breaks in 2020 and a significant albeit weak positive trend since June. On the individual level, the series showed strong differences in temporal dynamics and a high degree of randomness and day-specific sensitivity. The results stress the importance of Twitter as a means of communication during the pandemic and illustrate that these beliefs travel very fast and are quickly endorsed.de
dc.languageende
dc.subject.ddcPublizistische Medien, Journalismus,Verlagswesende
dc.subject.ddcNews media, journalism, publishingen
dc.subject.ddcPolitikwissenschaftde
dc.subject.ddcPolitical scienceen
dc.subject.otherCOVID-19; Conspiracy beliefs; Regular Paper; Time series analysis; Twitter structural break analysis; Word embeddingde
dc.titleConspiracy theories on Twitter: emerging motifs and temporal dynamics during the COVID-19 pandemicde
dc.description.reviewbegutachtet (peer reviewed)de
dc.description.reviewpeer revieweden
dc.identifier.urllocalfile:/var/tmp/crawlerFiles/deepGreen/bf047dca846d45b6a50896cd2b43f0b2/bf047dca846d45b6a50896cd2b43f0b2.pdfde
dc.source.journalInternational Journal of Data Science and Analytics
dc.source.volume13de
dc.publisher.countryCHEde
dc.source.issue4de
dc.subject.classozinteraktive, elektronische Mediende
dc.subject.classozInteractive, electronic Mediaen
dc.subject.classozpolitische Willensbildung, politische Soziologie, politische Kulturde
dc.subject.classozPolitical Process, Elections, Political Sociology, Political Cultureen
dc.subject.thesozSoziale Mediende
dc.subject.thesozsocial mediaen
dc.subject.thesozTwitterde
dc.subject.thesoztwitteren
dc.subject.thesozDesinformationde
dc.subject.thesozdisinformationen
dc.subject.thesozFalschmeldungde
dc.subject.thesozfalse reporten
dc.identifier.urnurn:nbn:de:0168-ssoar-88564-5
dc.rights.licenceCreative Commons - Namensnennung 4.0de
dc.rights.licenceCreative Commons - Attribution 4.0en
ssoar.contributor.institutionGESISde
internal.statusformal und inhaltlich fertig erschlossende
internal.identifier.thesoz10094228
internal.identifier.thesoz10094030
internal.identifier.thesoz10063936
internal.identifier.thesoz10063949
dc.type.stockarticlede
dc.type.documentZeitschriftenartikelde
dc.type.documentjournal articleen
dc.source.pageinfo315-333de
internal.identifier.classoz1080404
internal.identifier.classoz10504
internal.identifier.journal2725
internal.identifier.document32
internal.identifier.ddc070
internal.identifier.ddc320
dc.source.issuetopicOnline Information Disorder: Fake News, Bots, and Trollsde
dc.identifier.doihttps://doi.org/10.1007/s41060-021-00298-6de
dc.description.pubstatusVeröffentlichungsversionde
dc.description.pubstatusPublished Versionen
internal.identifier.licence16
internal.identifier.pubstatus1
internal.identifier.review1
ssoar.wgl.collectiontruede
internal.dda.referencecrawler-deepgreen-217@@bf047dca846d45b6a50896cd2b43f0b2


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