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Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2020: ECML PKDD 2020 Workshops pp 374–384Cite as

Experimental Evaluation of Scale, and Patterns of Systematic Inconsistencies in Google Trends Data

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1323))

Abstract

Search analytics and trends data is widely used by media, politicians, economists, and scientists in various decision-making processes. The data providers often use sampling when calculating the request results, due to the huge data volume that would need to be processed otherwise. The representativity of such samples is typically assured by the providers. Often, limited or no information about the reliability and validity of the service or the sampling confidence are provided by the services and, as a consequence, the data quality has to be assured by the users themselves, before using it for further analysis.

In this paper, we develop an experimental setup to estimate and measure possible variation in service results for the example of Google Trends. Our work demonstrates that the inconsistencies in Google Trends Data and the resulting contradictions in analyses and predictions are systematic and particularly large when analyzing timespans of eights months or less. In our experiments, the representativity claimed by the service was disproved in many cases. We found that beyond search volume and timespan, there are additional factors for the deviations that can only be explained by Google itself. When working with Google Trends data, users must be aware of the marked risks associated with the inconsistencies in the samples.

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Notes

  1. 1.

    https://trends.google.de.

  2. 2.

    https://www.finanznachrichten.de/nachrichten-2020-04/49427722-google-trends-as-a-proxy-for-covid-19-incidence-and-deaths-378.htm (accessed 17.07.2020).

  3. 3.

    There is a discussion thread at Google support: https://support.google.com/google-ads/thread/8389370?msgid=26184434 (accessed 17.07.2020).

  4. 4.

    https://mangools.com/blog/kwfinder-top-questions/ A direct retrieval from Google Ads was not possible for us since Google only provides very rough figures such as “10,000–100,000” by default - only larger advertisers receive more precise data.

  5. 5.

    For April 16, 17 and 18, 2020 there are 35 query results each, which have the Google index value 0 for each hour. The corresponding timespans were, therefore, not considered in the analysis.

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Acknowledgements

This work is partly funded by the European Research Council under grant agreement 833635 (ROXANNE) and 832921(MIRROR) and by the Lower Saxony Ministry of Science and Culture under grant number ZN3492 within the Lower Saxony “Vorab” of the Volkswagen Foundation, supported by the Center for Digital Innovations (ZDIN).

Author information

Authors and Affiliations

  1. HASE & IGEL GmbH, Oldenburg, Germany

    Philipp Behnen & Jan Schoenmakers

  2. VLBA, Oldenburg University, Oldenburg, Germany

    Rene Kessler, Felix Kruse & Jorge Marx Gómez

  3. L3S Research Center, Leibniz University Hannover, Hanover, Germany

    Sergej Zerr

Authors
  1. Philipp Behnen
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  2. Rene Kessler
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  3. Felix Kruse
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  4. Jorge Marx Gómez
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  5. Jan Schoenmakers
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  6. Sergej Zerr
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Corresponding author

Correspondence to Philipp Behnen .

Editor information

Editors and Affiliations

  1. University of Sydney, Sydney, NSW, Australia

    Irena Koprinska

  2. Monash University, Clayton, VIC, Australia

    Michael Kamp

  3. University of Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  4. University of Bari Aldo Moro, Bari, Italy

    Corrado Loglisci

  5. University of Guelph, Guelph, ON, Canada

    Luiza Antonie

  6. University of Caen Normandy, Caen, France

    Albrecht Zimmermann

  7. University of Pisa, Pisa, Italy

    Riccardo Guidotti

  8. Norwegian University of Science and Technology, Trondheim, Norway

    Özlem Özgöbek

  9. University of Porto, Porto, Portugal

    Rita P. Ribeiro

  10. UPC BarcelonaTech, Barcelona, Spain

    Ricard Gavaldà

  11. University of Porto, Porto, Portugal

    João Gama

  12. Fraunhofer IAIS, St. Augustin, Germany

    Linara Adilova

  13. Royal Holloway University of London, Egham, UK

    Yamuna Krishnamurthy

  14. University of Lisbon, Lisbon, Portugal

    Pedro M. Ferreira

  15. University of Bari Aldo Moro, Bari, Italy

    Donato Malerba

  16. University of Lisbon, Lisbon, Portugal

    Ibéria Medeiros

  17. University of Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  18. ICAR-CNR, Rende, Italy

    Giuseppe Manco

  19. University of Naples Federico II, Naples, Italy

    Elio Masciari

  20. University of North Carolina, Charlotte, NC, USA

    Zbigniew W. Ras

  21. Australian National University, Canberra, ACT, Australia

    Peter Christen

  22. Leibniz University Hannover, Hannover, Germany

    Eirini Ntoutsi

  23. Technical University of Dortmund, Dortmund, Germany

    Erich Schubert

  24. University of Southern Denmark, Odense, Denmark

    Arthur Zimek

  25. University of Pisa, Pisa, Italy

    Anna Monreale

  26. Warsaw University of Technology, Warsaw, Poland

    Przemyslaw Biecek

  27. ISTI-CNR, PISA, Italy

    Salvatore Rinzivillo

  28. Berlin Institute of Technology, Berlin, Germany

    Benjamin Kille

  29. Berlin Institute of Technology, Berlin, Germany

    Andreas Lommatzsch

  30. Norwegian University of Science and Technology, Trondheim, Norway

    Jon Atle Gulla

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Behnen, P., Kessler, R., Kruse, F., Gómez, J.M., Schoenmakers, J., Zerr, S. (2020). Experimental Evaluation of Scale, and Patterns of Systematic Inconsistencies in Google Trends Data. In: Koprinska, I., et al. ECML PKDD 2020 Workshops. ECML PKDD 2020. Communications in Computer and Information Science, vol 1323. Springer, Cham. https://doi.org/10.1007/978-3-030-65965-3_25

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  • DOI: https://doi.org/10.1007/978-3-030-65965-3_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65964-6

  • Online ISBN: 978-3-030-65965-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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