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Analyzing Policy-Making III: Digital Network Analysis

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The Palgrave Handbook of Methods for Media Policy Research

Abstract

This chapter presents a methodological approach to analyze the structure of the online news domain before and after a media policy was passed. I use tools from network science to this end and I discuss the implication of this regulation for the public sphere. Networks are simplified representations of processes, which capture basic connections among those who take part in them. Through network analysis, one can assess the underlying forces that drive those processes and their consequences. In this chapter, network analysis is applied to the study of the structure of the online news domain and its organizing principles. I leverage digital trace data to map the hyperlinks that media outlets send to each other. I build on this data and use network community detection methods to inform the policy debate around a potential European regulation on news-link activity. Links are journalistic and social devices through which news consumption can be enhanced. They potentially promote a necessary normative condition envisioned by democracy theorists: the exposure to diverse news information. European policy attempts to regulate news-linking activity online could potentially limit them and therefore, curtail their positive function. In Spain, those attempts turned to action. A recent Spanish copyright regulation imposes a tax over news-linking activity in the country. More recently, the European Parliament announced that it might steer toward the same policy course. By focusing on the Spanish case, this chapter illustrates how network science can help to inform decisions on news-link regulation. More broadly, it shows how tools borrowed from this discipline are useful for media policy research. Specially when it comes to the study of the online sphere. To this end, I assess the changes in the fragmentation of the news domain before and after the Spanish copyright regulation came into force. The results suggest the supply side of the digital news domain is more fragmented after the law was enforced. The nature of the data though does not allow to disentangle the causal mechanisms driving this change.

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References

Further Reading

  • Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). Analyzing social networks. London: Sage.

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  • González-Bailón, S. (2017). Decoding the social world: Data science and the unintended consequences of communication (S. Braman, Ed.) (first). Cambridge, MA: MIT Press.

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  • Kadushin, C. (2012). Understanding social networks: Theories, concepts and findings. Oxford: Oxford University Press.

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  • Monge, P. R., & Contractor, N. S. (2003). Theories of communication networks. Oxford: Oxford University Press.

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  • Salganik, M. J. (2017). Bit by bit: Social research in the digital age. Princeton: Princeton University Press.

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  • Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.

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Acknowledgements

The research reported in this paper was partly funded by the Spanish Ministry of Economy and Competitiveness under grant CSO2013-47082-P. This work was partly conducted while the author was visiting the Annenberg School for Communication at the University of Pennsylvania.

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Majó-Vázquez, S. (2019). Analyzing Policy-Making III: Digital Network Analysis. In: Van den Bulck, H., Puppis, M., Donders, K., Van Audenhove, L. (eds) The Palgrave Handbook of Methods for Media Policy Research. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-16065-4_28

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