Identifying Networks in Social Media: The case of #Grexit

  • Georgios MagkonisEmail author
  • Karen Jackson


We examine the intensity of ‘#Grexit’ usage in Twitter during a period of economic and financial turbulence. Using a frequency-analysis technique, we illustrate that we can extract detailed information from social media data. This allows us to map the networks of interest as it is reflected in Twitter. Our findings identify high-interest in Grexit from Twitter users in key peripheral countries, core Eurozone members as well as core EU member states outside the Eurozone. Overall, our study presents a useful tool for identifying clusters. This is part of a new research agenda utilising the information extracted from big data available via social media channels.


Networks Big data Twitter Geo-location data Grexit 


  1. Aguiar-Conraria L, Soares MJ (2014) The continuous wavelet transform: moving beyond uni-and bivariate analysis. J Econ Surv 28:344–375CrossRefGoogle Scholar
  2. Aguiar-Conraria L, Magalhães PC, Soares MJ (2012) Cycles in politics: wavelet analysis of political time series. Am J Polit Sci 56:500–518CrossRefGoogle Scholar
  3. Bennani T, Després M, Dujardin M, Duprey T, Kelber A (2014) Macroprudential framework: key questions applied to the French case. Occasional papers, 1Google Scholar
  4. Berger J, Morgan J (2015) The ISIS twitter census: defining and describing the population of ISIS supporters on twitter. Brookings Project US Relat Islamic World 3:20Google Scholar
  5. Borgatti SP, Everett MG (2000) Models of core/periphery structures. Soc Networks 21:375–395CrossRefGoogle Scholar
  6. Burnside C, Eichenbaum M, Rebelo S (2004) Government guarantees and self-fulfilling speculative attacks. J Econ Theory 119:31–63CrossRefGoogle Scholar
  7. Caraiani P (2012) Money and output: new evidence based on wavelet coherence. Econ Lett 116:547–550CrossRefGoogle Scholar
  8. Chang R, Velasco A (2001) A model of financial crises in emerging markets. Q J Econ 116:489–517CrossRefGoogle Scholar
  9. Da Z, Engelberg J, Gao P (2011) In search of attention. J Financ 66:1461–1499CrossRefGoogle Scholar
  10. Dergiades T, Milas C, Panagiotidis T (2014) Tweets, google trends, and sovereign spreads in the GIIPS. Oxf Econ Pap gpu046:1–27Google Scholar
  11. Earnshaw RA, Lei C, Li J, Migassabi S, Vourdas A (2012) Large-scale data analysis using the winger function. Physica A 391:2401–2407CrossRefGoogle Scholar
  12. European Commission (2014) Directorate-general economic and financial affairs. Flash Eurobarometer 400, Introduction of the Euro in the more recently acceded member statesGoogle Scholar
  13. European Commission (2015a) European economy, macroeconomic imbalances country report-Belgium 2015. Occasional Paper 212Google Scholar
  14. European Commission (2015b) Directorate-general economic and financial affairs. Flash Eurobarometer 418, Introduction of the Euro in the more recently acceded member statesGoogle Scholar
  15. European Commission (2016) European economy, fiscal sustainability report 2015. Institutional Paper 018Google Scholar
  16. Fire M, Puzis R, Elovici Y (2016) Organization mining using online social networks. Netw Spat Econ 16:545–578CrossRefGoogle Scholar
  17. Foy H (2015) Candidates put euro at centre of polish presidential race. Financial Times, 1 AprilGoogle Scholar
  18. Holl A, Mariotti I (2017) The geography of logistics firm location: the role of accessibility. Netw Spat Econ 18:1–25Google Scholar
  19. Illenberger J, Nagel K, Flotterod G (2013) The role of spatial interaction in social networks. Netw Spat Econ 13:255–282CrossRefGoogle Scholar
  20. Joseph K, Wintoki MB, Zhang Z (2011) Forecasting abnormal stock returns and trading volume using investor sentiment: evidence from online search. Int J Forecast 27:1116–1127CrossRefGoogle Scholar
  21. Ko J, Kwon HW, Kim HS, Lee K, Choi MY (2014) Model for twitter dynamics: public attention and time series of tweeting. Physica A 404:142–149CrossRefGoogle Scholar
  22. Krugman P (1996) Are currency crises self-fulfilling? NBER Macroeconomics Annual 1996, Volume 11. MIT press, CambridgeGoogle Scholar
  23. Laney D (2012) The importance of ‘big data’: a definition. Gartner, StanfordGoogle Scholar
  24. Micossi S (2015) What future for the eurozone?
  25. Monroe BL, Pan J, Roberts ME, Sen M, Sinclair B (2015) No! Formal theory, causal inference, and big data are not contradictory trends in political science. PS Polit Sci Polit 48:71–74CrossRefGoogle Scholar
  26. Moya-Gómez B, Salas-Olmedo MH, García-Palomares JC, Gutiérrez J (2017) Dynamic accessibility using big data: the role of the changing conditions of network congestion and destination attractiveness. Netw Spat Econ pp 1–18.
  27. Pattison P (1993) Algebraic models for social networks. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  28. Rua A, Nunes LC (2009) International comovement of stock market returns: a wavelet analysis. J Empir Financ 16:632–639CrossRefGoogle Scholar
  29. Shlomo N, Goldstein H (2015) Editorial: big data in social research. J R Stat Soc Ser A 178:787–790CrossRefGoogle Scholar
  30. Tett G (2013) Markets insight: wake up to the Twitter effect on markets. Financial Times, 18 AprilGoogle Scholar
  31. Wang XB, Cao X, Yin K, Adams TM (2017) Modeling vehicle miles traveled on local roads using classification roadway spatial structure. Netw Spat Econ 17(3):713–735CrossRefGoogle Scholar
  32. Westland CJ, Hao JX, Xiao X, Shan S (2016) Substitutes, complements and network effects in instant messaging services. Netw Spat Econ 16:525–543CrossRefGoogle Scholar
  33. Wheatley J (2015) Spectre of Grexit sparks fears for central and eastern Europe. Financial Times, 21 JuneGoogle Scholar
  34. Zhan X, Ukkusuri SV, Zhu F (2014) Inferring urban land use using large-scale social media check-in data. Netw Spat Econ 14:647–667CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Subject Group of Economics & Finance, Portsmouth Business SchoolUniversity of PortsmouthPortsmouthUK
  2. 2.Department of Economics & Quantitative Methods, Westminster Business SchoolUniversity of WestminsterLondonUK

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