Addressing Google Trends inconsistencies

Abstract

Google Trends reports the evolution of the popularity of internet searches. Its main output is the Search Volume Index (SVI), a relative measure of the popularity of a term computed using a sample of the searches. Due to the sampling, the SVI series are not entirely consistent, as the same query produces different results that can widely change from day to day. This paper investigates the nature of these inconsistencies by modeling and simulating the data-generating process. Simulations are applied to describe how a typical time series is distorted due to the sampling process and to quantify how averaging extractions smoothes the series. Finally, a relationship between term popularity, series dispersion, and the averaged extractions is derived, so recommendations for constructing consistent SVIs can be provided.