Online interest for electronic cigarettes using Google trends in the UK: a correlation analysis
Author(s)
Type
Journal Article
Abstract
Background:
Google Trends provides an easily accessible and cost-effective method of providing real-time insight into user interest.
Objective:
to address the gap in UK prevalence data for e-cigarettes by analyzing Google Trends to identify correlations with official data from Action on Smoking and Health. The study further evaluates Google Trend’s sensitivity to real-time events and the ability for predictive models to forecast future data based on Google Trends.
Methods:
UK Google Trends data from 2012 to 2021 was analyzed to assess (a) the most popular electronic nicotine device terminology; (b) statistically significant points in time; (c) correlations between Relative Search Volumes and official reports on electronic cigarette use and (d) whether Google Trends could predict future patterns in data. These were achieved using Locally Weighted Scatterplot Smoothing regression, Pruned Exact Linear Time Method, cross correlation, and Autoregressive Integrated Moving Average algorithms respectively.
Results:
“Vape” was revealed to be the most popular electronic nicotine device terminology with a correlation coefficient greater than +0.9 when compared to official electronic cigarette consumption data within a one-year timescale (lag 0). Results from ARIMA modeling were varied with the algorithms forecasted trends line occasionally lying outside of a 95% prediction interval.
Conclusion:
Google Trends may correspond to population-based prevalence of electronic cigarette use. The changing trends coincide with changing policy decisions. Google Trends based prediction for online interest in electronic cigarettes requires further validation so should currently be used in conjunction with other traditional methods of data collections.
Google Trends provides an easily accessible and cost-effective method of providing real-time insight into user interest.
Objective:
to address the gap in UK prevalence data for e-cigarettes by analyzing Google Trends to identify correlations with official data from Action on Smoking and Health. The study further evaluates Google Trend’s sensitivity to real-time events and the ability for predictive models to forecast future data based on Google Trends.
Methods:
UK Google Trends data from 2012 to 2021 was analyzed to assess (a) the most popular electronic nicotine device terminology; (b) statistically significant points in time; (c) correlations between Relative Search Volumes and official reports on electronic cigarette use and (d) whether Google Trends could predict future patterns in data. These were achieved using Locally Weighted Scatterplot Smoothing regression, Pruned Exact Linear Time Method, cross correlation, and Autoregressive Integrated Moving Average algorithms respectively.
Results:
“Vape” was revealed to be the most popular electronic nicotine device terminology with a correlation coefficient greater than +0.9 when compared to official electronic cigarette consumption data within a one-year timescale (lag 0). Results from ARIMA modeling were varied with the algorithms forecasted trends line occasionally lying outside of a 95% prediction interval.
Conclusion:
Google Trends may correspond to population-based prevalence of electronic cigarette use. The changing trends coincide with changing policy decisions. Google Trends based prediction for online interest in electronic cigarettes requires further validation so should currently be used in conjunction with other traditional methods of data collections.
Date Issued
2023-12
Date Acceptance
2023-09-01
Citation
Substance Use and Misuse, 2023, 58 (14), pp.1791-1797
ISSN
1082-6084
Publisher
Taylor and Francis Group
Start Page
1791
End Page
1797
Journal / Book Title
Substance Use and Misuse
Volume
58
Issue
14
Copyright Statement
© 2023 The Author(s). Published with license by Taylor & Francis Group, LLC This is an Open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in anyway. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:001059546100001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Subjects
e-cigarette
google trends
Life Sciences & Biomedicine
Psychiatry
Psychology
Science & Technology
Social Sciences
Substance Abuse
vape
Publication Status
Published
Date Publish Online
2023-09-23