Social Network Analysis

  • Richard Medina
  • Nigel WatersEmail author
Living reference work entry


This chapter elucidates how social network analytics influence regional science. The current state of the art in social network analysis, both quantitative and qualitative, is examined. How social network analysis can be and is used to influence and to understand the spatial manifestations of economic and political activity across regions in continuous and network space is explained. The major social networks now extant are described, and their current activities are discussed including the topics of microtargeting, transparency, privacy, proprietary algorithms, and “fake news” and its synonyms such as post-truth. Policy implications to influence and govern these activities, such as taxation and regulation, are reviewed and their regional economic implications discussed.


Social networks Fake news Network science Geodemographic Microtargeting Data mining 



Application programming interfaces


American Telephone and Telegraph


British Broadcasting Corporation


European Union


Fake News


General Data Protection Regulation


Independent cascade


Influence maximization


Linear threshold


New York Times


Regional science


Social media


Social Media/Social Networks


Social network


Social network analysis


Structured query language


User-generated content


Virtual Reality


  1. Alhajj R, Rokne J (eds) (2018) Encyclopedia of social network analysis and mining, 2nd edn. Springer, New York. first edition published 2014Google Scholar
  2. Barnes TJ (2003) What's wrong with American regional science? A view from science studies. Can J Reg Sci 26(1):3–26Google Scholar
  3. Boyd D, Ellison NB (2007) Social network sites: definition, history and scholarship. J Comp Mediat Comm 13(1):210–230CrossRefGoogle Scholar
  4. Eggers D (2013) The circle. Random House, New YorkGoogle Scholar
  5. Huang Q (2018) Social media analytics. In: Wilson JP (ed) The geographic information science & technology body of knowledge (1st Quarter 2018 Edition).
  6. Kitchin R (2017) Leveraging finance and producing capital. In: Kitchin R, Lauriault TP, Wilson MW (eds) Understanding spatial media. Sage, London, pp 178–187CrossRefGoogle Scholar
  7. Levin S (2018) Is Facebook a publisher? In public it says no, but in court it says yes. Accessed 4 Nov 2018
  8. Li Y, Fan J, Wang Y, Tan KL (2018) Influence maximization on social graphs: a survey. IEEE Transactions on Knowledge and Data Engineering. Accessed 19 Nov 2018
  9. McLuhan M (2006) The medium is the message. In: Media and cultural studies: keyworks, revised edn. Blackwell, Oxford, pp 107–116Google Scholar
  10. Metaxas PT, Mustafaraj E (2012) Social media and the elections. Science 338(6106):472–473CrossRefGoogle Scholar
  11. Taylor DJ (2018) Fayke Newes. The History Press, Brimscombe PortGoogle Scholar
  12. Waters NM (2013) Chapter 38, Social network analysis. In: Fischer MM, Nijkamp P (eds) Handbook of regional science. Springer, Heidelberg, pp 725–740Google Scholar
  13. Zafarani R, Abbasi MA, Liu H (2014) Social media mining: an introduction. Cambridge University Press, Cambridge. Accessed 15 Nov 2018CrossRefGoogle Scholar

Further Reading

  1. Arthur WB (1994) Increasing returns and path dependence. The University of Michigan Press, Ann ArborCrossRefGoogle Scholar
  2. Barabási AL (2003) Linked: the new science of networks. Perseus Books Group, New YorkGoogle Scholar
  3. Beckett L (2012) Everything we know (So Far) about Obama’s big data tactics. Accessed 3 Nov 2018
  4. Boyd D (2018) Amplification and responsible journalism. Accessed 17 Nov 2018
  5. Cervone G, Sava E, Huang Q, Schnebele E, Harrison J, Waters NM (2016) Using Twitter for tasking remote-sensing data collection and damage assessment: 2013 Boulder flood case study. Int J Remote Sens 37(1):100–124. Scholar
  6. Dunbar RI (1993) Coevolution of neocortical size, group size and language in humans. Behav Brain Sci 16(4):681–694CrossRefGoogle Scholar
  7. Edsall TB (2012) Let the Nanotargeting begin. Accessed 21 Nov 2018
  8. Handley L (2017) Trump’s digital campaign director was paid $1,500 to set up his election website. Then he raked in $94 million when Trump won. Accessed 21 Nov 2018
  9. Martinez AG (2016) Chaos monkeys: obscene fortune and random failure in Silicon Valley. HarperCollins, New YorkGoogle Scholar
  10. Parker R (2012) Social and anti-social media. Accessed 21 Nov 2018
  11. Parscale B (2017) Trump digital director says Facebook helped win the White House. Accessed 3 Nov 2018
  12. Pew Research Center (2018a) Social media: fact sheet. Accessed 14 Nov 2018
  13. Pew Research Center (2018b) Journalism and media: newspapers fact sheet. Accessed 12 Nov 2018
  14. Verge (2018) The monopoly-busting case against Google, Amazon, Uber, and Facebook. What tech companies have to fear from antitrust law. Accessed 21 Nov 2018
  15. Warner MR (2018) Potential policy proposals for regulation of social media and technology firms. Accessed 16 Nov 2018
  16. Waters NM (2018) Tobler’s first law of geography. In: Richardson D, Castree N, Goodchild MF, Kobayashi A, Liu W, Marston R (eds) International encyclopedia of geography: people, the earth, environment, and technology. Wiley, New York. Scholar
  17. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440CrossRefGoogle Scholar
  18. Weiss MJ (2000) The clustered world. Little, Brown, and Company, New YorkGoogle Scholar
  19. Wikipedia (2018) Most popular social networks. Accessed 4 Nov 2018
  20. Wu T (2019) The curse of bigness: antitrust in the new gilded age. Columbia Global Reports, Columbia University, New YorkGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of GeographyUniversity of UtahSalt Lake CityUSA
  2. 2.Department of GeographyUniversity of CalgaryCalgaryCanada

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