Publicação
Using Candidates’ Tweets to Predict an election outcome. The United States 2022 Midterm elections study
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Rita, Paulo Miguel Rasquinho Ferreira | |
| dc.contributor.advisor | António, Nuno Miguel da Conceição | |
| dc.contributor.author | Afonso, Francisco Rodrigo Fernandes | |
| dc.date.accessioned | 2023-11-13T16:12:49Z | |
| dc.date.embargo | 2026-10-24 | |
| dc.date.issued | 2023-10-24 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Marketing Intelligence | pt_PT |
| dc.description | This thesis has originated the article «Using Candidates’ Tweets to Predict an Election Outcome» in Political Research Quarterly: https://doi.org/10.1177/10659129241286827 | pt_PT |
| dc.description.abstract | Understanding 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. | pt_PT |
| dc.identifier.tid | 203385675 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/159896 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Machine Learning | pt_PT |
| dc.subject | Predictive Modelling | pt_PT |
| dc.subject | Topic Analysis | pt_PT |
| dc.subject | Political Communication | pt_PT |
| dc.subject | Social Media Political Marketing | pt_PT |
| dc.subject | pt_PT | |
| dc.title | Using Candidates’ Tweets to Predict an election outcome. The United States 2022 Midterm elections study | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | embargoedAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Marketing Analítico, especialização em Inteligência de Marketing | pt_PT |