Skip to main content
SpringerLink
Log in
Book cover

The Palgrave Handbook of Methods for Media Policy Research pp 347–366Cite as

Palgrave Macmillan

Analyzing Big Data

  • Chapter
  • First Online:

Abstract

This chapter looks into how big data and data science methods can be used to support law and policy research with empirical evidence on digital media production and consumption. To this end we analyze two cases. The simple case concerns the automatic scraping of news media websites to gather data on what is being published by news organizations. The complex case is about Robin, a research infrastructure which allows volunteers to donate their web browsing data stream so the process of personalized communications online can be studied. We discuss the issues researchers need to consider during the planning, data collection, and analysis phases of big data based research. We conclude that despite the limitations, difficulties and well-justified critique, social scientists, legal scholars, and researchers working in the humanities need to develop individual skills, and institutional competencies in big data methods, because data science is quickly becoming to be an indispensable part of the methodological tool-set of these disciplines.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired Magazine, 16(7), 16-07.

    Google Scholar 

  • Berry, D. (2011, July 11). The computational turn: Thinking about the digital humanities. Culture Machine, 12 [online]. Available at http://www.culturemachine.net/index.php/cm/article/view/440/470.

  • Bodó, B., Helberger, N., & de Vreese, C. H. (2017). Political micro-targeting: A Manchurian candidate or just a dark horse? Internet Policy Review, 6(4).

    Google Scholar 

  • Bodó, B., Helberger, N., Irion, K., Borgesius Zuiderveen, F. J., Moller, J., van der Velde, B., … de Vreese, C. H. (2017). Tackling the algorithmic control crisis—The technical, legal, and ethical challenges of research into algorithmic agents. Yale Journal of Law & Technology, 19, 133.

    Google Scholar 

  • Borgesius, F. J., Trilling, D., Möller, J., Bodó, B., de Vreese, C. H., & Helberger, N. (2016). Should we worry about filter bubbles? An interdisciplinary inquiry into self-selected and pre-selected personalised communication. Internet Policy Review, 5(1).

    Google Scholar 

  • boyd, d., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118x.2012.678878.

    Article  Google Scholar 

  • Cadwalladr, C., & Graham-Harrison, E. (2018). How Cambridge analytica turned Facebook ‘likes’ into a lucrative political tool. The Guardian, 18. https://www.theguardian.com/technology/2018/mar/17/facebook-cambridge-analytica-kogan-data-algorithm.

  • Chen, C. L. P., & Zhang, C.-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on big data. Information Sciences, 275, 314–347. https://doi.org/10.1016/j.ins.2014.01.015.

    Article  Google Scholar 

  • Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J. M., & Welton, C. (2009). MAD skills. Proceedings of the VLDB Endowment, 2(2), 1481–1492. https://doi.org/10.14778/1687553.1687576.

    Article  Google Scholar 

  • Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007.

    Article  Google Scholar 

  • Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72–80. https://doi.org/10.1016/j.ijpe.2014.04.018.

    Article  Google Scholar 

  • Jacobs, A. (2009). The pathologies of big data. Communications of the ACM, 52(8), 36. https://doi.org/10.1145/1536616.1536632.

    Article  Google Scholar 

  • Kitchin, R. (2014). Big data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 2053951714528481.

    Article  Google Scholar 

  • Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google Flu: Traps in big data analysis. Science, 343(6176), 1203–1205.

    Article  Google Scholar 

  • Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. New York, NY: McKinsey & Company.

    Google Scholar 

  • Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Boston: Houghton Mifflin Harcourt.

    Google Scholar 

  • Mayer-Schönberger, V., & Cukier, K. (2014). Learning with big data: The future of education. Boston: Houghton Mifflin Harcourt.

    Google Scholar 

  • McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68.

    Google Scholar 

  • Shah, D. V., Cappella, J. N., & Neuman, W. R. (2015). Big data, digital media, and computational social science: Possibilities and perils. The ANNALS of the American Academy of Political and Social Science, 659(1), 6–13. https://doi.org/10.1177/0002716215572084.

    Article  Google Scholar 

  • Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508.

    Article  Google Scholar 

Further Reading

  • Bodó, B., Helberger, N., Irion, K., Borgesius Zuiderveen, F. J., Moller, J., van der Velde, B., … de Vreese, C. H. (2017). Tackling the algorithmic control crisis—The technical, legal, and ethical challenges of research into algorithmic agents. Yale Journal of Law & Technology, 19, 133.

    Google Scholar 

  • Borgesius, F. Z., Gray, J., & Eechoud, M. V. (2015). Open data, privacy, and fair information principles: Towards a balancing framework. Berkeley Technology Law Journal, 30, 2073.

    Google Scholar 

  • Kitchin, R. (2014). Big data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 2053951714528481.

    Article  Google Scholar 

  • Mitchell, R. (2018). Web scraping with python: Collecting more data from the modern web. Sebastopol: O’Reilly Media.

    Google Scholar 

  • Shah, D. V., Cappella, J. N., & Neuman, W. R. (2015). Big data, digital media, and computational social science: Possibilities and perils. The ANNALS of the American Academy of Political and Social Science, 659(1), 6–13. https://doi.org/10.1177/0002716215572084.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Information Law, University of Amsterdam, Amsterdam, The Netherlands

    Balázs Bodó

  2. Lead Architect Data Science, SLTN Inter Access, Hilversum, The Netherlands

    Bob van de Velde

Authors
  1. Balázs Bodó
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bob van de Velde
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Communication, Drexel University, Philadelphia, USA

    Hilde Van den Bulck

  2. Department of Communication and Media Research, University of Fribourg, Fribourg, Switzerland

    Manuel Puppis

  3. Department of Communication Studies, Vrije Universiteit Brussel, Brussels, Belgium

    Karen Donders

  4. Department of Communication Studies, Vrije Universiteit Brussel, Brussels, Belgium

    Leo Van Audenhove

Rights and permissions

Reprints and permissions

Copyright information

© 2019 The Author(s)

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bodó, B., van de Velde, B. (2019). Analyzing Big Data. 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_20

Download citation

Publish with us

Policies and ethics

Search

Navigation