  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.
