Simulating the inconsistencies of Google Trends data

Abstract

[EN] Google Trends (GT) allows users to obtain reports of the evolution of the popularity of searchers made through the Google Search engine. Its main output is the Search Volume Index (SVI), a relative measure of the popularity of a term, which is computed using a sample of the searches. Due to the sampling error, the reports are not completely consistent, as the same query produces different time series that can widely change from day to day. This paper simulates the process of generating the SVI time series in the same way as GT does. By doing this, it has been shown that the sampling error could be an important issue if the popularity of the term under study is relatively low. Averaging multiple extractions from GT can only partially alleviate this.