How to measure political polarization in text-as-data? A scoping review of computational social science approaches
Autor(a) principal: | |
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Data de Publicação: | 2024 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/31198 |
Resumo: | The rise of political polarization within western societies has been portrayed by events such as the United States Capitol riot or the United Kingdom’s exit from the European Union. In this context, we argue that computational social science (CSS) methods offer a scalable and language- independent fashion to measure political polarization, enabling the processing of big data. In this vein, this article offers the first scoping review of the application of CSS methods to analyzing political polarization through text as data. We propose a categorization framework and reflect on the advantages and disadvantages of the different models used in the literature. Additionally, we underline the importance of filling research gaps, such as considering the temporal characteristic of political polarization, using a mathematical approach to analyze the use cases, and avoiding location and platform bias. We also provide recommendations for future research. |
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How to measure political polarization in text-as-data? A scoping review of computational social science approachesPolitical polarizationComputational social science (CSS) methodsText analysisDiscourseTwitterThe rise of political polarization within western societies has been portrayed by events such as the United States Capitol riot or the United Kingdom’s exit from the European Union. In this context, we argue that computational social science (CSS) methods offer a scalable and language- independent fashion to measure political polarization, enabling the processing of big data. In this vein, this article offers the first scoping review of the application of CSS methods to analyzing political polarization through text as data. We propose a categorization framework and reflect on the advantages and disadvantages of the different models used in the literature. Additionally, we underline the importance of filling research gaps, such as considering the temporal characteristic of political polarization, using a mathematical approach to analyze the use cases, and avoiding location and platform bias. We also provide recommendations for future research.Taylor and Francis2025-02-12T00:00:00Z2024-01-01T00:00:00Z20242024-02-28T12:19:27Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/31198eng1933-168110.1080/19331681.2024.2318404Pereira, C. T.Da Silva, R.Rosa, C. P. Dainfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-03T01:16:50Zoai:repositorio.iscte-iul.pt:10071/31198Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:12:13.552463Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
How to measure political polarization in text-as-data? A scoping review of computational social science approaches |
title |
How to measure political polarization in text-as-data? A scoping review of computational social science approaches |
spellingShingle |
How to measure political polarization in text-as-data? A scoping review of computational social science approaches Pereira, C. T. Political polarization Computational social science (CSS) methods Text analysis Discourse |
title_short |
How to measure political polarization in text-as-data? A scoping review of computational social science approaches |
title_full |
How to measure political polarization in text-as-data? A scoping review of computational social science approaches |
title_fullStr |
How to measure political polarization in text-as-data? A scoping review of computational social science approaches |
title_full_unstemmed |
How to measure political polarization in text-as-data? A scoping review of computational social science approaches |
title_sort |
How to measure political polarization in text-as-data? A scoping review of computational social science approaches |
author |
Pereira, C. T. |
author_facet |
Pereira, C. T. Da Silva, R. Rosa, C. P. Da |
author_role |
author |
author2 |
Da Silva, R. Rosa, C. P. Da |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Pereira, C. T. Da Silva, R. Rosa, C. P. Da |
dc.subject.por.fl_str_mv |
Political polarization Computational social science (CSS) methods Text analysis Discourse |
topic |
Political polarization Computational social science (CSS) methods Text analysis Discourse |
description |
The rise of political polarization within western societies has been portrayed by events such as the United States Capitol riot or the United Kingdom’s exit from the European Union. In this context, we argue that computational social science (CSS) methods offer a scalable and language- independent fashion to measure political polarization, enabling the processing of big data. In this vein, this article offers the first scoping review of the application of CSS methods to analyzing political polarization through text as data. We propose a categorization framework and reflect on the advantages and disadvantages of the different models used in the literature. Additionally, we underline the importance of filling research gaps, such as considering the temporal characteristic of political polarization, using a mathematical approach to analyze the use cases, and avoiding location and platform bias. We also provide recommendations for future research. |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-01-01T00:00:00Z 2024 2024-02-28T12:19:27Z 2025-02-12T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10071/31198 |
url |
http://hdl.handle.net/10071/31198 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1933-1681 10.1080/19331681.2024.2318404 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/embargoedAccess |
eu_rights_str_mv |
embargoedAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Taylor and Francis |
publisher.none.fl_str_mv |
Taylor and Francis |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799137771005149184 |