Métodos computacionales en ComunicaciónPresentación

  1. Elias Said Hung 1
  2. Daladier Jabba-Molinares 2
  1. 1 Facultad de Educación. Universidad Internacional de la Rioja, UNIR
  2. 2 Universidad del Norte
    info

    Universidad del Norte

    Barranquilla, Colombia

    ROR https://ror.org/031e6xm45

Aldizkaria:
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ISSN: 1697-8293

Argitalpen urtea: 2020

Zenbakien izenburua: Métodos computacionales en Comunicación

Alea: 18

Zenbakia: 1

Orrialdeak: 1-9

Mota: Artikulua

DOI: 10.7195/RI14.V18I1.1537 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Beste argitalpen batzuk: Icono14

Laburpena

The recent increase in data, tools, and processing power available digitally is encouraging the use of computational methods for the study of communication and in the Social Sciences, in general. A phenomenon that open new lines of research and a practical application. For example the understanding of social aspects in current digital contexts; the identification of factors that affect the occurrence of such events; the application of communication strategies, in the study of new meanings of citizen exercise and consumption of users from current digital scenarios; and in the use of new methodologies that until recently were alien to the field of Social and Humanistic Sciences. This special issue attempts to address the central issue of this issue, from some perspectives established by the authors that are part of this issue, in order to contribute to an overview of more relevant approaches and perspectives of applicability of these types of methods to Communication level today.

Erreferentzia bibliografikoak

  • Barroso Asenjo, P. (2011). Códigos de deontología periodística: análisis comparativo. Universitas. Revista de Ciencias Sociales y Humanas, (15), 141-176.
  • Bryson, S., Kenwright, D., Cox, M., Ellsworth, D., & Haimes, R. (1999). Visually exploring gigabyte data sets in real time. Communication of the ACM, 42(8), 82-90.
  • Casero-Ripollés, A. (2018). Research on political information and social media: Key points and challenges for the future. El profesional de la información, 27(5), 964-974.
  • Coddington, M. (2015). Clarifying journalism’s quantitative turn: A typology for evaluating data journalism, computational journalism, and computer-assisted reporting. Digital Journalism, 3 (3), 331-348. Recuperado de http://dx.doi.org/10.1080/21670811.2014.976400.
  • Charniak, E., & McDermott, D. (1985). Introduction to artificial intelligence. Estados Unidos: Addison‐Wesley, Estados Unidos.
  • Hendler, J. (1999). Is there an intelligent agent in your future?. Nature. Recuperado de http://www.math.pku.edu.cn/teachers/linzq/teaching/stm/references/agents.pdf.
  • Kuhn T. (1986). La Estructura de las Revoluciones Científicas. México: Fondo de Cultura Económica.
  • Nilsson, N. (1998). Artificial Intelligence: A New Synthesis. Estados Unidos: Morgan Kaufmann, Estados Unidos.
  • Niño, M. (2015). Cronología de antecedentes, origen y desarrollo del Big Data. Recuperado de http://www.mikelnino.com/2015/09/cronologia-big-data.html
  • Press, G. (2013). A very short history of Big Data. Forbes. Recuperado de https://www.forbes.com/sites/gilpress/2013/05/09/a-very-short-history-of-big-data/#7f2deb5a65a1
  • Russell, S., & Norvig, P. (2004). Inteligencia Artificial. Un enfoque moderno (Segunda edición). Madrid: Pearson Educación.
  • Torra, V. (2011). La inteligencia Artificial. Lychnos. Cuadernos de la Fundación General CSIC, 7. Recuperado de http://www.fgcsic.es/lychnos/es_es/articulos/inteligencia_artificial.
  • Turing, A. (1950). Computing Machinery and Intelligence. Ming. A Quarterly Review, 49, 433-460.
  • Villanueva, E. (2002). Deontología informativa: códigos deontológicos de la prensa escrita en el mundo. México: Universidad Iberoamericana.
  • Ye, M., & Abbe, E. (2018). Communication-Computation Efficient Gradient Coding. arXiv preprint arXiv:1802.03475v1.
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