A novel business cycle surveillance system using the query logs of search engines

Business indices and indicators are used to monitor the regime shifts of business cycles. Generally, the indices and indicators are comprised of various economic variables that are compiled by different government departments. Compiling the variables involves a great deal of data processing, which delays the monitoring of business cycles. In this paper, we propose a novel business cycle surveillance system that utilizes the query logs of search engines for business cycle modeling. The system employs an effective feature selection technique to identify query terms that are representative of business cycles. The selected terms and the frequency count of queries associated with the terms are then integrated to classify the status of business cycles. We use data discretization techniques to reduce the sparseness of query frequencies. Experimental results based on a five-year dataset show that the proposed system can classify the status of business cycles accurately, and the selected query terms reveal interesting human behavior patterns in different business cycles. Unlike economic variables, query logs are readily available through online Web services, so our system can provide business cycle information in a timely manner.