Big Data, Algorithms and the Metrics of Social Media News

  • Diana Bossio


In ‘Big Data, Algorithms and the Metrics of Social Media News’ (Chapter 5), Bossio illustrates that while news organisations are increasingly pushing content out through a number of social media platforms to improve the number of views of news content, they are also competing with other stakeholders hoping to influence the way news discourses are represented. Using a media analysis of the Brexit vote as a case study, Bossio argues that the new challenge for professional journalism is the increasingly savvy manipulation by other political stakeholders of social media algorithms, especially those that focus on prioritisation and filtering of news to influence audience consumption habits.


  1. Anderson, C. (2006). The long tail: How the future of business is selling less of more. New York, NY: Hyperion.Google Scholar
  2. Anderson, C. W. (2013). Towards a sociology of computational and algorithmic journalism. New Media and Society, 15, 1005–1021.CrossRefGoogle Scholar
  3. Backstrom, L. (2013, August 7). News feed FYI: A window into news feed. Facebook Business. Retrieved from
  4. Bringing Big Data to the Enterprise. (2017). IBM. Retrieved from
  5. Bucher, T. (2012). A technicity of attention: How software ‘makes sense’. Culture Machine, 13. Retrieved from
  6. Burgess, M. (2016, April 4). How the 11.5 million Panama Papers were analysed. Wired Offshore. Retrieved from
  7. Carbon Emissions: past, present and future-interactive. (2014, December 1). The Guardian. Retrieved from
  8. Carlson, M. (2015). The robotic reporter: Automated journalism and the redefinition of labour, compositional forms, and journalistic authority. Digital Journalism, 3, 416–431.CrossRefGoogle Scholar
  9. Chen, M., Shiwen, M., & Liu, Y. (2014). Big Data: A survey. Mobile Networks and Applications, 19, 171–209.CrossRefGoogle Scholar
  10. Cheney-Lippold, J. (2011). A new algorithmic identity: Soft biopolitics and the modulation of control. Theory, Culture & Society, 28(6), 164–181.CrossRefGoogle Scholar
  11. Coddington, M. (2015). Clarifying journalism’s quantitative turn. A typology for evaluating data journalism, computational journalism, and computer-assisted reporting. Digital Journalism, 3(3), 331–338.Google Scholar
  12. Collins, M. (2016, February 26). The truth about Britain First—The one-man band with a knack for Facebook. The Guardian. Retrieved from
  13. Deakon, D., Wring, D., Harmer, E., Stanyer, J., & Downey, J. (2016, June 16). Hard evidence: Analysis shows extent of press bias for Brexit. The Conversation. Retrieved from
  14. Diakopoulos, N. (2015). Algorithmic accountability: Journalistic investigation of computational power structures. Digital Journalism, 3(3), 398–415.CrossRefGoogle Scholar
  15. Dorr, K. N. (2016). Mapping the field of algorithmic journalism. Digital Journalism, 4(6), 700–722.CrossRefGoogle Scholar
  16. EU Referendum: The Results in Maps and Charts. (2016, June 24). BBC. Retrieved from
  17. Feinberg, A. (2017, March 31). This is almost certainly FBI Director James Comey’s Twitter account. Gizmodo.
  18. Gillespie, T. (2010). The politics of ‘platforms’. New Media & Society, 12(3), 347–364.CrossRefGoogle Scholar
  19. Gillespie, T. (2014). The relevance of algorithms. In T. Gillespie, P. Boczkowski, & K. A. Foot (Eds.), Media technologies: Essays on communication, materiality, and society. Cambridge, MA: MIT Press. Retrieved from
  20. Goodway, F. (2014, October 28). This is how Britain First plans to infiltarte your Facebook feed. Mirror. Retrieved from
  21. GoogleTrends. (2016, June 24). Top questions on the European Union. Twitter. Retrieved from
  22. Greenberg, A. (2016, April 4). How reporters pulled off the Panama Papers, the biggest leak in whistleblower history. Wired. Retrieved from
  23. Harding, L. (2016, April 5). What are the Panama Papers? A guide to history’s biggest data leak. The Guardian. Retrieved from
  24. Henley, J. (2016, April 6). Iceland PM steps aside after protests over Panama Papers revelations. The Guardian. Retrieved from
  25. Hermida, A. (2014). Tell everyone: Why we share and why it matters. Toronto: Doubleday Canada.Google Scholar
  26. Interview with Steve Suo. (2005, July 7). Frontline. Retrieved from
  27. Jack, A. (2016a, January 2). 2016: Predictions and planning. Financial Times. Retrieved from
  28. Jack, I. (2016b, June 25). In this Brexit, the poor turned on an elite that ignored them. The Guardian. Retrieved from
  29. Kanter, J. (2017, March 2). Leave.EU used ‘creepy’ Facebook profiling technology to win Brexit campaign—And now the government is investigating. Business Insider. Retrieved from
  30. Lewis, S. (2015). Journalism in an era of big data: Cases, concepts and critiques. Digital Journalism, 3(3), 321–330.CrossRefGoogle Scholar
  31. Lewis, S., & Usher, N. (2013). Open source and journalism: Toward new frameworks for imagining news innovation. Media, Culture and Society, 35(5), 602–619.CrossRefGoogle Scholar
  32. Lewis, S., & Westlund, O. (2014). Big data and journalism: Epistemology, expertise, economics and ethics. Digital Journalism, 3(3), 447–466.CrossRefGoogle Scholar
  33. Marciano, J. (2016, June 28). Who were the big online winners from Brexit? Similar Web. Retrieved from:
  34. Meyer, P. (1979). Precision journalism: A reporter’s introduction to social science methods. Bloomington: Indiana University Press.Google Scholar
  35. Meyer, P. (2011, December 10). Riot theory is relative. The Guardian. Retrieved from
  36. Miller, C. C. (2015, July 9). When algorithms discriminate. The New York Times. Retrieved from
  37. Napoli, P. M. (2014). Automated media: An institutional theory perspective on algorithmic media production and consumption. Communication Theory, 24(3), 340–360.CrossRefGoogle Scholar
  38. Parasie, S., & Dagiral, E. (2013). Data-driven journalism and the public good: “Computer-assisted-reporters” and “programmer-journalists” in Chicago. New Media & Society, 15(6), 853–871.CrossRefGoogle Scholar
  39. Perraudin, F. (2016, December 9). Northern England’s Brexit voters need to be heard, says thinktank. The Guardian. Retrieved from
  40. Polonski, V. W. (2016, June 9). #Nofilter debate: Brexit campaigners dominate on Instagram. The Conversation. Retrieved from
  41. Sweney, M. (2016, June 29). Brexit breaks news records as Facebook helps drive leave campaign. The Guardian. Retrieved from
  42. Tambini, D. (2016, November 18). In the new robopolitics, social media has left newspapers for dead. The Guardian. Retrieved from
  43. Tandoc, E. C., & Thomas, R. J. (2015). The ethics of web analytics: Implications of using audience metrics in news construction. Digital Journalism, 3(2), 243–258.CrossRefGoogle Scholar
  44. Ten Predictions for 2016. (2016, January 1). Al Jazeera English. Retrieved from
  45. The Hard-Headed Case for Britain to Stay in the EU. (2016, January 2). Financial Times. Retrieved from
  46. Withnall, A. (2015, November 10). Britain First far-right group claims to be ‘first political party’ to reach 1 million likes on Facebook. The Independent. Retrieved from

Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.Swinburne UniversityHawthornAustralia

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