Crowd-Squared: A New Method for Improving Predictions by Crowd-sourcing Google Trends Keyword Selection

Advances in information technologies and analytic tools have dramatically increased our ability to obtain accurate data on billions of economic decisions almost the instant that they are made. Services such as Google Trends aggregate billions of search queries and provide information about the search volume of different terms. This information from the “crowd”, had been successfully used to accurately predict a wide variety of events. Nevertheless, a major challenge for successful utilization of this source of information is the generation of an appropriate list of search terms that resemble the phenomena of interest. Current methods for search term generation often use proprietary search engine data or employ “black box” classifiers or require extensive computational power. We introduce Crowd-Squared, a new crowd-based method, for selecting relevant search terms. We show that this method is successful in various domains and that it performs as well or even outperforms selection methods used in previous studies. Crowd-Squared: A New Method for Improving Predictions