A Cloud-Based Dashboard for Time Series Analysis on Hot Topics from Social Media

  • Yunkai LiuEmail author
  • Weifeng Xu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)


Social Media has increasingly acquired enormous storehouses of shared interactive ideas among people in virtual communities over the past decade. While considering hot topics in social media as the culture of today’s world, related analysis derived from its trend allows researchers and society understand public point of view regarding emerging topics through online social interactions. The ease of categorizing the trends of topics makes it beneficial to find how people react to a certain topic. In our research, the distributions of keywords from hot topics are studied. First of all, a private cloud system is set up to collect and to filter raw data from social network (Twitter). Then, a web-based dashboard is developed to demonstrate static numbers and related charts. Some experiments are performed based on the newly-developed system deployed. Distribution functions of keywords are studied based on 2016 data, and further applications are applied based on 2017 spring data. Further cross comparisons are deployed based on daily, weekly, and monthly frequencies from different locations.


Time series analysis Social media Hot topics Trending topics Twitter Google Trends 


  1. 1.
    Anstead, N., O’Loughlin, B.: Social media analysis and public opinion: the 2010 UK general election. J. Comput.-Mediat. Commun. 20(2), 204–220 (2014)CrossRefGoogle Scholar
  2. 2.
    Malhotra, N.K., Malhotra, N.K.: Basic Marketing Research: Integration of Social Media. Pearson, Boston (2012)zbMATHGoogle Scholar
  3. 3.
    He, W., Zha, S., Li, L.: Social media competitive analysis and text mining: a case study in the pizza industry. Int. J. Inf. Manag. 33(3), 464–472 (2013)CrossRefGoogle Scholar
  4. 4.
    Miller, W., Pellen, R.: Google: a chronology of innovations, acquisitions, and growth. Google Scholar and More, pp. 11–36. Routledge, 2 January 2014Google Scholar
  5. 5.
    Kai Jie Shawn, L., Stridsberg, D.: Feeling the market’s pulse with Google Trends. Int. Fed. Tech. Anal.’ J. (2015)Google Scholar
  6. 6.
    Kumar, S., Morstatter, F., Liu, H.: Twitter Data Analytics. Springer, New York (2014)CrossRefGoogle Scholar
  7. 7.
    De Boor, C., De Boor, C., Mathématicien, E.U., De Boor, C., De Boor, C.: A Practical Guide to Splines. Springer, New York (1978)CrossRefGoogle Scholar
  8. 8.
    Kreyszig, E.: Advanced Engineering Mathematics, 9 edn, p. 816. Wiley. ISBN 9780471488859Google Scholar
  9. 9.
    Petzold, C.: Canonical Splines in WPF and Silverlight (2009)Google Scholar
  10. 10.
    Cryer, J.D., Chan, K.-S.: Time Series Analysis. Springer (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Gannon UniversityErieUSA
  2. 2.University of BaltimoreBaltimoreUSA

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