Calibration of Google Trends Time Series

TL;DR

This paper introduces G-TAB, a calibration method for Google Trends data that overcomes rounding issues, enabling accurate comparison of multiple queries on a common scale through an offline anchor bank and efficient online calibration.

Contribution

The paper presents G-TAB, a novel two-phase method that calibrates Google Trends data for multiple queries, addressing rounding errors and improving data utility.

Findings

G-TAB effectively calibrates multiple queries on a common scale.

Few Google Trends requests are needed for accurate calibration.

The method is validated through empirical evaluation.

Abstract

Google Trends is a tool that allows researchers to analyze the popularity of Google search queries across time and space. In a single request, users can obtain time series for up to 5 queries on a common scale, normalized to the range from 0 to 100 and rounded to integer precision. Despite the overall value of Google Trends, rounding causes major problems, to the extent that entirely uninformative, all-zero time series may be returned for unpopular queries when requested together with more popular queries. We address this issue by proposing Google Trends Anchor Bank (G-TAB), an efficient solution for the calibration of Google Trends data. Our method expresses the popularity of an arbitrary number of queries on a common scale without being compromised by rounding errors. The method proceeds in two phases. In the offline preprocessing phase, an "anchor bank" is constructed, a set of queries spanning the full spectrum of popularity, all calibrated against a common reference query by carefully chaining together multiple Google Trends requests. In the online deployment phase, any given search query is calibrated by performing an efficient binary search in the anchor bank. Each search step requires one Google Trends request, but few steps suffice, as we demonstrate in an empirical evaluation. We make our code publicly available as an easy-to-use library at https://github.com/epfl-dlab/GoogleTrendsAnchorBank.