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Using Candidates’ Tweets to Predict an Election Outcome

2025-03Artigo científico Acesso aberto
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dc.contributor.authorAfonso, Francisco
dc.contributor.authorRita, Paulo
dc.contributor.authorAntónio, Nuno
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.pblSage
dc.date.accessioned2024-11-11T23:04:01Z
dc.date.available2024-11-11T23:04:01Z
dc.date.issued2025-03
dc.descriptionAfonso, F., Rita, P., & António, N. (2025). Using Candidates’ Tweets to Predict an Election Outcome. Political Research Quarterly, 78(1), 323-340. https://doi.org/10.1177/10659129241286827 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 (DOI: 10.54499/UIDB/04152/2020) - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS).
dc.description.abstractUnderstanding social media’s role in political communication is crucial in the evolving media landscape. Motivated by the transformative impact of social media on political engagement and discourse, this research fills an under-explored academic gap, studying the effects of geographic focus—local versus national—in candidates’ tweets on U.S. Senate election outcomes. It reveals a modest but significant correlation between the nature of political discourse and election competitiveness. Interestingly, strict adherence to party-centric topics did not significantly influence electoral success. The study assessed the performance of regression and classification models in forecasting election outcomes, with classification models demonstrating superior results. Both models provide a new benchmark for future studies in political communication on social media. These findings bear considerable implications for political practitioners, indicating that election success is not merely guaranteed by echoing party-centric issues or predominantly adopting a national communication scope.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent18
dc.format.extent1127956
dc.identifier.doi10.1177/10659129241286827
dc.identifier.issn1065-9129
dc.identifier.otherPURE: 99417065
dc.identifier.otherPURE UUID: f4894d3a-3a69-47fe-96f8-69bac8040a40
dc.identifier.otherScopus: 85209183222
dc.identifier.otherWOS: 001349755600001
dc.identifier.otherORCID: /0000-0001-6050-9958/work/171475353
dc.identifier.urihttp://hdl.handle.net/10362/175008
dc.identifier.urlhttps://www.scopus.com/pages/publications/85209183222
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001349755600001
dc.identifier.urlhttps://doi.org/10.5281/zenodo.13738284
dc.identifier.urlhttps://github.com/franciscorfafonso/using-candidates-tweets-to-predict-election/tree/Published
dc.language.isoeng
dc.peerreviewedyes
dc.relationhttps://doi.org/10.54499/UID/04152/2025
dc.relationInformation Management Research Center
dc.subjectMachine Learning
dc.subjectPredictive Modelling
dc.subjectTopic Analysis
dc.subjectPolitical Communication
dc.subjectSocial Media Political Marketing
dc.subjectTwitter
dc.subjectSociology and Political Science
dc.subjectSDG 16 - Peace, Justice and Strong Institutions
dc.titleUsing Candidates’ Tweets to Predict an Election Outcomeen
dc.typejournal article
degois.publication.firstPage323
degois.publication.issue1
degois.publication.lastPage340
degois.publication.titlePolitical Research Quarterly
degois.publication.volume78
dspace.entity.typePublication
oaire.awardNumberUIDB/04152/2020
oaire.awardTitleInformation Management Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication3274bdb3-4dd3-4bbe-8f74-d34190081f87
relation.isProjectOfPublication.latestForDiscovery3274bdb3-4dd3-4bbe-8f74-d34190081f87

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