Communication research using BigData methodology

Authors

DOI:

https://doi.org/10.4185/RLCS-2015-1076

Keywords:

Big Data, data mining, contents analysis, methodology, epistemology, scientific representation, communication research

Abstract

Digital technologies are enabling new tools and analysis methodologies in the field of communication, as well as in other research areas. In this document, we approach aspects related to communication research using the Big Data methodology, which is generating much expectation and showing good results in other scientific areas. Methodology: A set of articles were used as corpus for this work and we applied the Big Data analysis and representation techniques as contents analysis methodology over this sample in order to evaluate its relevance and effectiveness. Conclusions: As a result of our study, we can conclude that this methodology can be useful in our field of knowledge, but it has a limited effectiveness though.

Downloads

Download data is not yet available.

Author Biography

Francisco López Cantos, Jaume I University

Professor in Audiovisual Communication and Advertising. Throughout these years, he has published numerous articles that range from community media and university televisions and their role for development, to aspects related to audiovisual production technologies, multimedia communication and digital journalism, and in recent times it deals mainly with aspects related to the image and the representation of knowledge and scientific dissemination.

References

Anscombe, F. J. (1973): "Graphs in Statistical Analysis,". En: American Statistician, 27, pp. 17-21. DOI: https://doi.org/10.1080/00031305.1973.10478966

Burrows, R. and Savage, M. (2014): “After the crisis? Big Data and the methodological challenges of empirical sociology”. En: Big Data & Society, pp. 1–6. DOI: https://doi.org/10.1177/2053951714540280

D’heer, E. and Verdegem, P. (2014): “Conversations about the elections on Twitter: Towards a structural understanding of Twitter’s relation with the political and the media field”. En: European Journal of Communication. 29(6), pp. 720–734. DOI: https://doi.org/10.1177/0267323114544866

Feyerabend, P. K. (1975): Tratado contra el método. Esquema de una teoría anarquista del conocimiento. Madrid: Tecnos, ed. 1981.

Fisher, E. (2015): “‘You Media’: audiencing as marketing in social media”. En: Media, Culture & Society, 37(1) pp. 50–67. DOI: https://doi.org/10.1177/0163443714549088

Focault, B. & Meirelles, I. (2015): “Visualizing Computational Social Science: The Multiple Lives of a Complex Image”. En: Science Communication, 37(1), pp. 34-58. DOI: https://doi.org/10.1177/1075547014556540

Ford, B. J. (1992): Images of Science: A History of Scientific Illustration. London: British Library.

Gregg, M. (2015): “Inside the Data Spectacle”. En: Television & New Media, 16(1), pp. 37–51. DOI: https://doi.org/10.1177/1527476414547774

Gross, A. (2006): “The Verbal and the Visual in Science: A Heideggerian Perspective”. En: Science in Context, 19(4), pp. 443-474. DOI: https://doi.org/10.1017/S0269889706001037

Gurevitch, L. (2014): “Google Warming: Google Earth as eco-machinima”. En: Convergence, 20(1), pp. 85–107 DOI: https://doi.org/10.1177/1354856513516266

Kitchin, B. (2014): “Big Data, new epistemologies and paradigm shifts”. En: Big Data & Society, pp. 1–12. DOI: https://doi.org/10.1177/2053951714528481

Kuhn, T. S. (1962): The Structure of Scientific Revolutions. Chicago: Chicago University Press

Latour, B. (2009): “Tarde’s idea of quantification”. En: The Social after Gabriel Tarde: Debates and Assessments. London: ed. Candea, M. Routledge, pp. 145–162.

Leonelli, S. (2014): “What difference does quantity make? On the epistemology of Big Data in biology, en Big Data & Society, pp. 1–11. DOI: https://doi.org/10.1177/2053951714534395

Lynch, M. (2006): “The Production of Scientific Images. Vision and Re-Vision in the History, Philosophy, and Sociology of Science”, Visual Cultures of Science: rethinking representational practices in knowledge building and science communication. Hanover, N.H.: Dartmouth Collegue Press., p. 26 y ss.

Lyon, D. (2014): “Surveillance, Snowden, and Big Data: Capacities, consequences, critique”. En: Big Data & Society, pp. 1–13. DOI: https://doi.org/10.1177/2053951714541861

Manovich, L. (2002): “The Anti-Sublime Ideal in Data Art”, disponible en https://illinoissquaire.web.illinois.edu/wp/2018/11/15/the-anti-sublime-ideal-in-data-art/

Murthy, D. y Bowman, S. (2014): “Big Data solutions on a small scale: Evaluating accessible high-performance computing for social research”. En: Big Data & Society, pp. 1–12. DOI: https://doi.org/10.1177/2053951714559105

Penney, J. (2014): “Motivations for participating in ‘viral politics’: A qualitative case study of Twitter users and the 2012 US presidential election”, en Convergence, DOI: https://doi.org/10.1177/1354856514532074

Raghavan, P. (2014): “It’s time to scale the science in the social sciences”. En: Big Data & Society, pp 1–4. DOI: https://doi.org/10.1177/2053951714532240

Schroede, R. (2014): “Big Data and the brave new world of social media research”. En: Big Data & Society, pp. 1–11. DOI: https://doi.org/10.1177/2053951714563194

Taylor, L., Schroeder, R. y Meyer, E. (2014): “Emerging practices and perspectives on Big Data analysis in economics: Bigger and better or more of the same?”. En: Big Data & Society, pp. 1–10. DOI: https://doi.org/10.1177/2053951714536877

Vilches, L. (coord.) (2011): La investigación en comunicación. Métodos y técnicas en la era digital. Barcelona: Gedisa.

Zwitter, A. (2014): “Big Data ethics”. En: Big Data & Society, pp. 1–6. DOI: https://doi.org/10.1177/2053951714559253

Published

2015-12-21

How to Cite

López Cantos, F. (2015). Communication research using BigData methodology. Revista Latina De Comunicación Social, (70), 878–890. https://doi.org/10.4185/RLCS-2015-1076

Issue

Section

Miscellaneous