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
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- 2.
- 3.
There is a discussion thread at Google support: https://support.google.com/google-ads/thread/8389370?msgid=26184434 (accessed 17.07.2020).
- 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.
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).
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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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