Applications of Google Trends as a Data Source for Statistical Models

Authors

DOI:

https://doi.org/10.18778/0208-6018.368.04

Keywords:

Google Trends, statistical analysis, prognosis

Abstract

As technology advances, there is a growing number of potential data sources that can provide an alternative to traditional surveys. An example of this is the real time search popularity data made available through Google Trends. This type of data makes it possible to study public opinion, behaviour and attitudes in society or forecast economic phenomena.

A definite advantage of using search popularity data is the immediate availability and low cost of obtaining such data. Also of significance is the fact that the Google Trends tool allows for direct research into the behaviour of Internet users, and not just their declarations as in the case of a survey. This can make a difference if respondents consider one of the answers to be more morally correct. Nevertheless, the use of Google Trends requires selecting correct search topics and terms to be included in the study and an awareness of the fact that the research sample is limited to Google search engine users. The paper will present the advantages and disadvantages of Google Trends and review its usefulness as a data source especially in times of higher market volatility.

Downloads

Download data is not yet available.

References

BańBura M., Giannone D., Reichlin L. (2012), Nowcasting, [in:] M.P. Clements, D.F. Hendry (eds.), The Oxford Handbook of Economic Forecasting. Oxford Handbooks Online, Oxford University Press, Oxford, pp. 193–224.
Google Scholar

Brodeur A., Clark A.E., Fleche S., Powdthavee N. (2021), COVID–19, lockdowns and well-being: Evidence from Google Trends, “Journal of Public Economics”, vol. 193, 104346.
Google Scholar

Butler D. (2013), When Google got flu wrong: US outbreak foxes a leading web-based method for tracking seasonal flu, “Nature”, vol. 494, pp. 155–157.
Google Scholar

Carrière-Swallow Y., Labbé F. (2013), Nowcasting with Google Trends in an emerging market, “Journal of Forecasting”, vol. 32, pp. 289–298.
Google Scholar

Ettredge M., Gerdes J., Karuga G. (2005), Using web-based search data to predict macroeconomic statistics, “Communications of the ACM”, vol. 48, pp. 87–92.
Google Scholar

Ginsberg J., Mohebbi M.H., Patel R.S., Brammer L., Smolinski M.S., Brilliant L. (2009), Detecting influenza epidemics using search engine query data, “Nature”, vol. 457, pp. 1012–1014.
Google Scholar

Google_Trends_Data (2023), FAQ about Google Trends data, https://support.google.com/trends/answer/4365533?hl=en [accessed: 28.11.2023].
Google Scholar

Hu H., Tang L., Zhang S., Wang H. (2018), Predicting the direction of stock markets using optimized neural networks with Google Trends, “Neurocomputing”, vol. 285, pp. 188–195.
Google Scholar

Hyndman R.J., Khandakar Y. (2008), Automatic time series forecasting: the forecast package for R, “Journal of Statistical Software”, vol. 27, pp. 1–22.
Google Scholar

Hyndman R.J., Athanasopoulos G., Bergmeir C., Caceres G., Chhay L., O’Hara-Wild M., Petropoulos F., Razbash S., Wang E., Yasmeen F. (2024), forecast: Forecasting functions for time series and linear models. R package version 8.22.0, https://pkg.robjhyndman.com/forecast/ [accessed: 4.03.2024].
Google Scholar

Li X., Pan B., Law R., Huang X . (2017), Forecasting tourism demand with composite search index, “Tourism Management”, vol. 59, pp. 57–66.
Google Scholar

Mellon J. (2014), Internet search data and issue salience: The properties of Google Trends as a measure of issue salience, “Journal of Elections, Public Opinion & Parties”, vol. 24(1), pp. 45–72.
Google Scholar

Saegner T., Austys D. (2022), Forecasting and surveillance of COVID–19 spread using Google trends: literature review, “International Journal of Environmental Research and Public Health”, vol. 19(19), 12394.
Google Scholar

Vosen S., Schmidt T. (2011), Forecasting private consumption: survey‐based indicators vs. Google trends, “Journal of Forecasting”, vol. 30(6), pp. 565–578.
Google Scholar

Yu L., Zhao Y., Tang L., Yang Z. (2019), Online big data-driven oil consumption forecasting with Google trends, “International Journal of Forecasting”, vol. 35(1), pp. 213–223.
Google Scholar

Zhang W., Wang P. (2020), Investor attention and the pricing of cryptocurrency market, “Evolutionary and Institutional Economics Review”, vol. 17, pp. 445–468.
Google Scholar

Published

2024-10-01

How to Cite

Lenart, K. (2024). Applications of Google Trends as a Data Source for Statistical Models. Acta Universitatis Lodziensis. Folia Oeconomica, 69–81. https://doi.org/10.18778/0208-6018.368.04

Issue

Section

Articles

Similar Articles

<< < 28 29 30 31 32 33 34 35 36 37 38 39 40 41 > >> 

You may also start an advanced similarity search for this article.