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T his study investigates the potential of using data about Web searches to predict an important macroeconomic statistic, specifically the number of unemployed workers in the U.S. Our underlying assumption is that people reveal useful information about their needs, wants, interests, and concerns via their Internet behavior, and that terms submitted to search engines reflect this information. Research indicates that the percentage of Web site visitors who are referred by search engines increased from 67% in 2001 to 88% in 2004 [4], so this data potentially offers a rich and timely source of information. The study finds that Web-based search data is associated with future unemployment data over the 77-week study period. This very preliminary result suggests search-term data might be useful in predicting other important macroeconomic statistics.A large proportion of job-related information gathering is conducted using the Internet [1, 10, 12]. Of the 54% of the U.S. population that uses the Internet, 16% engages in online job search activities [12]. There is evidence that unemployment duration has decreased among some Internet job seekers [9].The Internet is credited with overcoming information bottlenecks in key areas of the labor market, affecting how worker-firm matches are made, how labor services are delivered, and how local markets shape demand [1].

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The increasing availability of consumer feedback on the web provides a wealth of information that organizations can use for product and service improvement. Many consumer feedback sites allow users to enter both a quantitative rating and a qualitative critique. Previous research has used this information disjunctively. This work proposes an innovative approach that integrates the two types of information to identify words that are related to positive or negative consumer ratings. A case study shows that this approach does raise some issues not identified using existing analytical approaches.