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SePoMa: Semantic-Based Data Analysis for Political Marketing

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Book cover Technologies and Innovation (CITI 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 883))

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Abstract

Political marketing is a discipline concerned with the study of the right political communication strategies. Precise decision making in political marketing largely depends upon the thorough analysis of vast amounts of data from a variety of sources. Relevant information from mass media, social networks, Web pages, etc., should be gathered and scrutinized in order to provide the insights necessary to properly adjust the political parties’ and politicians’ messages to society. The main challenges in this context are, first of all, the integration of data from disparate sources, and hence its analysis to extract the relevant information to use in the decision-making process. Big data and Semantic Web technologies provide the means to face these challenges. In this paper, we propose SePoMa, a framework that applies semantic Big data analysis techniques to the political domain to assist in the definition of political marketing strategies for political entities. SePoMa explores the pertinent structured, semi-structured and unstructured data sources and automatically populates the political ontology, which is then examined to generate electorate knowledge. An exemplary use case scenario is described that illustrates the benefits of the framework for the automation of electoral research and the support of political marketing strategies.

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Notes

  1. 1.

    https://catalog.data.gov/dataset/citizen-participation.

  2. 2.

    http://www.foaf-project.org/.

  3. 3.

    https://www.w3.org/TR/vocab-org/.

  4. 4.

    https://wiki.dbpedia.org/services-resources/ontology.

  5. 5.

    https://www.w3.org/TR/owl2-overview/.

  6. 6.

    https://protege.stanford.edu/.

  7. 7.

    http://mklab.iti.gr/project/prophet-ontology-populator.

  8. 8.

    http://www.scielo.org.mx/scielo.php?pid=S0187-57952014000200005&script=sci_arttext [Accessed: 17-Jul-2018].

  9. 9.

    http://www.eluniversal.com.mx/elecciones-2018/amlo-el-candidato-que-mas-crece-en-redes-sociales [Accessed: 17-Jul-2018].

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Acknowledgements

This work has been supported by the Spanish National Research Agency (AEI) and the European Regional Development Fund (FEDER/ERDF) through project KBS4FIA (TIN2016-76323-R).

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Authors and Affiliations

  1. Escuela Internacional de Doctorado, University of Murcia, 30100, Murcia, Spain

    Héctor Hiram Guedea-Noriega

  2. DIS, Faculty of Computer Science, University of Murcia, 30100, Murcia, Spain

    Francisco García-Sánchez

Authors
  1. Héctor Hiram Guedea-Noriega
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  2. Francisco García-Sánchez
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Corresponding author

Correspondence to Héctor Hiram Guedea-Noriega .

Editor information

Editors and Affiliations

  1. Facultad de Informática, Universidad de Murcia, Murcia, Spain

    Rafael Valencia-García

  2. Departamento de Filología Moderna, Universidad de Castilla la Mancha, Ciudad Real, Ciudad Real, Spain

    Gema Alcaraz-Mármol

  3. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Javier Del Cioppo-Morstadt

  4. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Néstor Vera-Lucio

  5. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Martha Bucaram-Leverone

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Guedea-Noriega, H.H., García-Sánchez, F. (2018). SePoMa: Semantic-Based Data Analysis for Political Marketing. In: Valencia-García, R., Alcaraz-Mármol, G., Del Cioppo-Morstadt, J., Vera-Lucio, N., Bucaram-Leverone, M. (eds) Technologies and Innovation. CITI 2018. Communications in Computer and Information Science, vol 883. Springer, Cham. https://doi.org/10.1007/978-3-030-00940-3_15

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  • DOI: https://doi.org/10.1007/978-3-030-00940-3_15

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