IDEAS home Printed from https://ideas.repec.org/a/kap/netspa/v19y2019i1d10.1007_s11067-018-9399-9.html
   My bibliography  Save this article

Identifying Networks in Social Media: The case of #Grexit

Author

Listed:
  • Georgios Magkonis

    (University of Portsmouth)

  • Karen Jackson

    (University of Westminster)

Abstract

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.

Suggested Citation

  • Georgios Magkonis & Karen Jackson, 2019. "Identifying Networks in Social Media: The case of #Grexit," Networks and Spatial Economics, Springer, vol. 19(1), pages 319-330, March.
    • Handle: RePEc:kap:netspa:v:19:y:2019:i:1:d:10.1007_s11067-018-9399-9
    DOI: 10.1007/s11067-018-9399-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11067-018-9399-9
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11067-018-9399-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Theologos Dergiades & Costas Milas & Theodore Panagiotidis, 2015. "Tweets, Google trends, and sovereign spreads in the GIIPS," Oxford Economic Papers, Oxford University Press, vol. 67(2), pages 406-432.
    2. Natalie Shlomo & Harvey Goldstein, 2015. "Editorial: Big data in social research," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 787-790, October.
    3. Luís Aguiar‐Conraria & Pedro C. Magalhães & Maria Joana Soares, 2012. "Cycles in Politics: Wavelet Analysis of Political Time Series," American Journal of Political Science, John Wiley & Sons, vol. 56(2), pages 500-518, April.
    4. Roberto Chang & Andres Velasco, 2001. "A Model of Financial Crises in Emerging Markets," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 116(2), pages 489-517.
    5. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    6. Burnside, Craig & Eichenbaum, Martin & Rebelo, Sergio, 2004. "Government guarantees and self-fulfilling speculative attacks," Journal of Economic Theory, Elsevier, vol. 119(1), pages 31-63, November.
    7. Ko, J. & Kwon, H.W. & Kim, H.S. & Lee, K. & Choi, M.Y., 2014. "Model for Twitter dynamics: Public attention and time series of tweeting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 142-149.
    8. Xianyuan Zhan & Satish Ukkusuri & Feng Zhu, 2014. "Inferring Urban Land Use Using Large-Scale Social Media Check-in Data," Networks and Spatial Economics, Springer, vol. 14(3), pages 647-667, December.
    9. Xiubin B. Wang & Xiaowei Cao & Kai Yin & Teresa M. Adams, 2017. "Modeling Vehicle Miles Traveled on Local Roads Using Classification Roadway Spatial Structure," Networks and Spatial Economics, Springer, vol. 17(3), pages 713-735, September.
    10. Johannes Illenberger & Kai Nagel & Gunnar Flötteröd, 2013. "The Role of Spatial Interaction in Social Networks," Networks and Spatial Economics, Springer, vol. 13(3), pages 255-282, September.
    11. Rua, António & Nunes, Luís C., 2009. "International comovement of stock market returns: A wavelet analysis," Journal of Empirical Finance, Elsevier, vol. 16(4), pages 632-639, September.
    12. Caraiani, Petre, 2012. "Money and output: New evidence based on wavelet coherence," Economics Letters, Elsevier, vol. 116(3), pages 547-550.
    13. Luís Aguiar-Conraria & Maria Joana Soares, 2014. "The Continuous Wavelet Transform: Moving Beyond Uni- And Bivariate Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 28(2), pages 344-375, April.
    14. Joseph, Kissan & Babajide Wintoki, M. & Zhang, Zelin, 2011. "Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1116-1127, October.
    15. Bennani, T. & Després, M. & Dujardin, M. & Duprey, T. & Kelber, A., 2014. "Macroprudential framework:key questions applied to the French case," Occasional papers 9, Banque de France.
    16. Paul Krugman, 1996. "Are Currency Crises Self-Fulfilling?," NBER Chapters, in: NBER Macroeconomics Annual 1996, Volume 11, pages 345-407, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Krüger, Jens J., 2024. "A Wavelet Evaluation of Some Leading Business Cycle Indicators for the German Economy," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 149438, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. De Santis, Roberto A., 2020. "Impact of the Asset Purchase Programme on euro area government bond yields using market news," Economic Modelling, Elsevier, vol. 86(C), pages 192-209.
    3. Moussa, Faten & BenOuda, Olfa & Delhoumi, Ezzeddine, 2017. "The use of open source internet to analysis and predict stock market trading volume," Research in International Business and Finance, Elsevier, vol. 41(C), pages 399-411.
    4. repec:zbw:bofrdp:2016_014 is not listed on IDEAS
    5. Sifat, Imtiaz Mohammad & Thaker, Hassanudin Mohd Thas, 2020. "Predictive power of web search behavior in five ASEAN stock markets," Research in International Business and Finance, Elsevier, vol. 52(C).
    6. González-Fernández, Marcos & González-Velasco, Carmen, 2020. "A sentiment index to measure sovereign risk using Google data," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 406-418.
    7. Theologos Dergiades & Eleni Mavragani & Bing Pan, 2017. "Arrivals of Tourists in Cyprus: Mind the Web Search Intensity," GreeSE – Hellenic Observatory Papers on Greece and Southeast Europe 107, Hellenic Observatory, LSE.
    8. Jens J. Krüger, 2021. "A Wavelet Evaluation of Some Leading Business Cycle Indicators for the German Economy," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(3), pages 293-319, December.
    9. Chang, Chun-Ping & Lee, Chien-Chiang, 2015. "Do oil spot and futures prices move together?," Energy Economics, Elsevier, vol. 50(C), pages 379-390.
    10. Verona, Fabio, 2016. "Time–frequency characterization of the U.S. financial cycle," Economics Letters, Elsevier, vol. 144(C), pages 75-79.
    11. Piersanti, Giovanni, 2012. "The Macroeconomic Theory of Exchange Rate Crises," OUP Catalogue, Oxford University Press, number 9780199653126.
    12. Tiwari, Aviral Kumar & Albulescu, Claudiu Tiberiu, 2016. "Oil price and exchange rate in India: Fresh evidence from continuous wavelet approach and asymmetric, multi-horizon Granger-causality tests," Applied Energy, Elsevier, vol. 179(C), pages 272-283.
    13. Papadamou, Stephanos & Fassas, Athanasios & Kenourgios, Dimitris & Dimitriou, Dimitrios, 2020. "Direct and Indirect Effects of COVID-19 Pandemic on Implied Stock Market Volatility: Evidence from Panel Data Analysis," MPRA Paper 100020, University Library of Munich, Germany.
    14. Ramos, Sofia B. & Latoeiro, Pedro & Veiga, Helena, 2020. "Limited attention, salience of information and stock market activity," Economic Modelling, Elsevier, vol. 87(C), pages 92-108.
    15. Szczygielski, Jan Jakub & Charteris, Ailie & Bwanya, Princess Rutendo & Brzeszczyński, Janusz, 2024. "Google search trends and stock markets: Sentiment, attention or uncertainty?," International Review of Financial Analysis, Elsevier, vol. 91(C).
    16. Chun-Ping Chang & Chien-Chiang Lee & GenFu Feng & Shao-Lin Ning, 2016. "Does higher government debt link to higher social expenditure? New method, new evidence," Applied Economics, Taylor & Francis Journals, vol. 48(16), pages 1429-1451, April.
    17. González-Fernández, Marcos & González-Velasco, Carmen, 2018. "Can Google econometrics predict unemployment? Evidence from Spain," Economics Letters, Elsevier, vol. 170(C), pages 42-45.
    18. Ari, Ali, 2012. "Early warning systems for currency crises: The Turkish case," Economic Systems, Elsevier, vol. 36(3), pages 391-410.
    19. Konstantinos N. Konstantakis & Despoina Paraskeuopoulou & Panayotis G. Michaelides & Efthymios G. Tsionas, 2021. "Bank deposits and Google searches in a crisis economy: Bayesian non‐linear evidence for Greece (2009–2015)," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5408-5424, October.
    20. Gandjon Fankem, Gislain Stéphane & Fouda Mbesa, Lucien Cédric, 2023. "Business cycle synchronization and African monetary union: A wavelet analysis," Journal of Macroeconomics, Elsevier, vol. 77(C).
    21. Verona, Fabio, 2016. "Time–frequency characterization of the U.S. financial cycle," Economics Letters, Elsevier, vol. 144(C), pages 75-79.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:netspa:v:19:y:2019:i:1:d:10.1007_s11067-018-9399-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.