Experiment for Analysing the Impact of Financial Events on Twitter

  • Ana Fernández-Vilas
  • Lewis Evans
  • Majdi Owda
  • Rebeca P. Díaz Redondo
  • Keeley Crockett
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10393)


Twitter, as the heart of publicly accessible Social Media, is one of the currently used platforms to share financial information and is a valuable source of information for different roles in the financial market. For all these roles, the quality analysis of Twitter as a source of financial information is essential to take decisions. The work in this paper is aligned with the ongoing work of the authors to a solution for irregularity monitoring in the financial market by harnessing data in online social media. To do so, the permeability of a variety of social media data feeders to financial irregularities should be analysed. That is the case of the experiment in this paper by putting the focus on Twitter microblogging platform and checking if this general purpose social media is permeable to a specific financial event. For this, we detail the analysis of Twitter permeability to a specific event in the past few months: the announcement about the merge of Tesco and Booker to create a UK’s Leading Food Business on the 27th January 2017. Both companies Tesco PLC and Booking Group PLC are listed in the main market of LSE (London Stock Exchange). Our findings provide promising evidences to address the problem of real-time detection of irregularities in the financial market via Twitter according to the volume (as a sign of the importance of the irregularity) and to other features (as signs of the potential origin causing the irregularity).


Twitter Stock market Financial irregularities Permeability 



This work was funded by Spanish Ministry of Education Culture and Sports, National Plan for Scientific and Technical Research and Innovation (Sub-Programme for Mobility) under the research stay grant PRIX16/00368. We thank the Manchester Metropolitan University (School of Computing Mathematics and Digital Technology) for its support during the research stay. This work is also partially funded by the Spanish Ministry of Economy and Competitiveness under the National Science Program (TEC2014-54335-C4-3-R).


  1. 1.
    Cazzoli, L., Sharma. R., Treccani, M., Lillo, F.A.: Large scale study to understand the relation between Twitter and financial market. In: Third European Network Intelligence Conference (2016)Google Scholar
  2. 2.
    Sprenger, T.O., Tumasjan, A., Sandner, P.G., Welpe, I.M.: Tweets and trades: the information content of stock microblogs. In: European Financial Management, vol. 20, pp. 926–957 (2014)Google Scholar
  3. 3.
    Hentschel, M., Alonso, O.: Follow the money: a study of cashtags on Twitter. First Monday 19(8) (2014)Google Scholar
  4. 4.
    Ruiz, E.J., Hristidis, V., Castillo, C., Gionis, A., Jaimes, A.: Correlating financial time series with microblogging activity. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining (2012)Google Scholar
  5. 5.
    Oliveira, N., Cortez, P., Areal, N.: The impact of microblogging data for stock market prediction: using Twitter to predict returns, volatility, trading volume and survey sentiment indices. Expert Syst. Appl. 73, 125–144 (2017)CrossRefGoogle Scholar
  6. 6.
    Liew, J.K.S., Budavári, T.: Do Tweet Sentiments Still Predict the Stock Market? SSRN (2016)Google Scholar
  7. 7.
    Rajesh, N., Gandy, L.: CashTagNN: using sentiment of tweets with CashTags to predict stock market prices. In: 11th International Conference on Intelligent Systems: Theories and Applications (2016)Google Scholar
  8. 8.
    Cortez, P., Oliveira, N., Ferreira, J.P.: Measuring user influence in financial microblogs: experiments using stocktwits data. In: Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics, WIMS 2016 (2016)Google Scholar
  9. 9.
    Adnan, M., Leak, A., Loingley, P.: A geocomputational analysis of Twitter activity around different world cities. Geo-Spatial Inf. Sci. 17(3), 145–152 (2014)CrossRefGoogle Scholar
  10. 10.
    Morstatter, F., Pfeffer, J., Liu, H., Carley, K.M.: Is the sample good enough? Comparing data from twitter’s streaming API with Twitter’s firehose. In: Proceedings of the 7th International Conference on Weblogs and Social Media, ICWSM 2013 (2013)Google Scholar
  11. 11.
    Liu, H., Morstatter, F., Tang, J., Zafarani, R.: The good, the bad, and the ugly: uncovering novel research opportunities in social media mining. Int. J. Data Sci. Anal. 1(3–4), 137–143 (2016)CrossRefGoogle Scholar
  12. 12.
    Vosoughi, S.: Automatic Detection and Verification of Rumors on Twitter. Massachusetts Institute of Technology (2015)Google Scholar
  13. 13.
    Tafti, A., Zotti, R., Jank, W.: Real-time diffusion of information on Twitter and the financial markets. PLoS ONE 11(8) (2016)Google Scholar
  14. 14.
    Jung, M.J., Naughton, J.P., Tahoun, A., Clare, A.W.: Do Firms Strategically Disseminate? Evidence from Corporate Use of Social Media. SSRN (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Information & Computing Laboratory, AtlantTIC Research CentreUniversity of VigoVigoSpain
  2. 2.School of Computing, Mathematics and Digital TechnologyManchester Metropolitan UniversityManchesterUK

Personalised recommendations