Scientometrics Analysis in Google Trends
Sozon Papavlasopoulos1★★ Corresponding author
- 1Department Information Science and Informatics, Ionian University, Ioannou, Theotoki, Kerkira, Corfu, GREECE.
CORRESPONDENCE
Sozon Papavlasopoulos
Department Information Science and Informatics, Ionian University, Ioannou, Theotoki, Kerkira, Corfu, GREECE.
Received: 04-10-2018; Revised: 26-02-2019; Accepted: 21-03-2019.
Volume 8, Issue 1 · pp. 27–37 · PUBLISHED · DOI: 10.5530/jscires.8.1.5
ABSTRACT
The extraction of the periodical non-stationarity feature of time series is obtained via the google trend data using keywords from modern sciences. This study aims to investigate when a keyword time series gives non-stationarity pics because this satisfies that the analysis of non-stationary categorical time series yields goodness of fit practice in the prediction issue. This method is implemented via an algorithm which is based on the extraction of the non- stationary distance as well as the formulation of the polynomial regression. The non-stationary algorithm is applied and the statistical evaluation is obtained using the non-parametric Cochran’s Q Test. The Q test leads to the conclusion that the Medicine and Biochemistry sciences are ranking in the top of the user’s preference followed by Physics, Mathematics and Social Sciences, while the emerging sciences such as Material Science are in the last rank positions.
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Papavlasopoulos, S. (). Scientometrics Analysis in Google Trends. Journal of Scientometric Research, 8(1), 27–37. https://doi.org/10.5530/jscires.8.1.5