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Psychographic traits identification based on political ideology: an author analysis study on Spanish politicians’ tweets posted in 2020

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dc.contributor.authorGarcía Díaz, José Antonio
dc.contributor.authorColomo Palacios, Ricardo
dc.contributor.authorValencia García, Rafael
dc.contributor.departmentInformática y Sistemas
dc.contributor.otherFacultades de la UMU::Facultad de Informática
dc.date.accessioned2026-01-14T11:38:31Z
dc.date.available2026-01-14T11:38:31Z
dc.date.copyright© 2021 The Authors
dc.date.issued2022-05
dc.description.abstractIn general, people are usually more reluctant to follow advice and directions from politicians who do not have their ideology. In extreme cases, people can be heavily biased in favour of a political party at the same time that they are in sharp disagreement with others, which may lead to irrational decision making and can put people’s lives at risk by ignoring certain recommendations from the authorities. Therefore, considering political ideology as a psychographic trait can improve political micro-targeting by helping public authorities and local governments to adopt better communication policies during crises. In this work, we explore the reliability of determining psychographic traits concerning political ideology. Our contribution is twofold. On the one hand, we release the PoliCorpus-2020, a dataset composed by Spanish politicians’ tweets posted in 2020. On the other hand, we conduct two authorship analysis tasks with the aforementioned dataset: an author profiling task to extract demographic and psychographic traits, and an authorship attribution task to determine the author of an anonymous text in the political domain. Both experiments are evaluated with several neural network architectures grounded on explainable linguistic features, statistical features, and state-of-the-art transformers. In addition, we test whether the neural network models can be transferred to detect the political ideology of citizens. Our results indicate that the linguistic features are good indicators for identifying fine-grained political affiliation, they boost the performance of neural network models when combined with embedding-based features, and they preserve relevant information when the models are tested with ordinary citizens. Besides, we found that lexical and morphosyntactic features are more effective on author profiling, whereas stylometric features are more effective in authorship attribution.
dc.formatapplication/pdf
dc.format.extent16
dc.identifier.citationFuture Generation Computer Systems, 2022, Vol. 130, pp. 59-74
dc.identifier.doihttps://doi.org/10.1016/j.future.2021.12.011
dc.identifier.eissn1872-7115
dc.identifier.issn0167-739X
dc.identifier.urihttp://hdl.handle.net/10201/186869
dc.languageeng
dc.publisherElsevier
dc.relationThis paper is part of the research project LaTe4PSP (PID2019- 107652RB-I00) funded by MCIN/ AEI/10.13039/501100011033. In addition, José Antonio García-Díaz is supported by Banco Santander and the University of Murcia through the Doctorado industrial programme.
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0167739X21004921?via%3Dihub
dc.rightsAttribution 4.0 International*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectAuthorship analysis
dc.subjectAuthor profiling
dc.subjectAuthorship attribution
dc.subjectLinguistic features
dc.subjectNatural language processing
dc.subject.odsNo relacionado con ningún objetivo de desarrollo sostenible
dc.titlePsychographic traits identification based on political ideology: an author analysis study on Spanish politicians’ tweets posted in 2020
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublicationes
relation.isAuthorOfPublication14ca7de1-eef1-42b4-9649-b765516ea4f3
relation.isAuthorOfPublicationab591422-699c-4535-8e8f-fd09f0e90ec2
relation.isAuthorOfPublication.latestForDiscovery14ca7de1-eef1-42b4-9649-b765516ea4f3
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