Browsing by Subject "Author profiling"

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    Language technology-based political microtargeting services
    (Springer Nature, 2024-10-24) José Antonio Miñarro-Giménez; Francisco García-Sánchez; Almela, Ángela; García Díaz, José Antonio; Marín Pérez, María José; Miñarro Giménez, José Antonio; Valencia García, Rafael; García Sánchez, Francisco; Alcaraz Mármol, Gema; Filología Inglesa
    In recent times, political behavior, from the act of voting to the participation of citizens in politics, has changed significantly. The proliferation and growth of Information and Communication Technologies (ICTs) has provided new and powerful tools to all stakeholders. In particular, social media allow a two-way communication channel between political parties and the electorate. Under these circumstances, accurate segmentation of electoral markets is essential for the development of campaign messages. To enable personalized one-to-one dialogue, it is necessary to characterize each user. However, this poses two major challenges. On the one hand, the degree of subjectivity in the political domain is difficult to determine because a fact can be considered positive or negative depending on the point of view. On the other hand, political polarization and partisanship, which refers to the fact that citizens are strongly biased in favor of certain political parties while strongly disagreeing with others. The goal of this work is to integrate and validate some of the Natural Language Processing (NLP) technologies developed and tested by the participating researchers in previous projects for the deployment and optimization of a commercial software platform for political microtargeting through author and user profiling.
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    Psychographic traits identification based on political ideology: an author analysis study on Spanish politicians’ tweets posted in 2020
    (Elsevier, 2022-05) García Díaz, José Antonio; Colomo Palacios, Ricardo; Valencia García, Rafael; Informática y Sistemas; Facultades de la UMU::Facultad de Informática
    In 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.