WebProNews

The University of California-Riverside has launched an online prediction tool to measure how the weak U.S. economy influences people's online behavior.

The "Web of Misery" is an online research project that invites people to predict how much traffic will increase to sites focused on topics surrounding economic distress, such as religion, guns, substance abuse and gambling.

The project was developed by Donna Hoffman, co-director at the Sloan Center for Internet Retailing.

"People have always been judging the likelihood of certain events in the online retailing space - how much will sales growth be on the online holiday season, to what will be the most popular online holiday shop and the most successful promotions," Hoffman told the BBC.

Hoffman says as the economy began to become weaker, she thought it would be interesting to look at Web sites as an online indicator of economic distress. "So, by actually seeing which website's traffic was going up, might indicate things were actually getting worse," she said.

There are ten categories in the project and each one has a Web site related to it. Predictions are taken from Web sites that include Foreclosure.com which tracks the number of foreclosed homes that are for sale across the U.S., Impactguns.com, a large online retailer of guns along with BibleGateway.com, a site that allows visitors to read and research scripture in multiple languages.

The growth of the Web sites are monitored by the number of unique visitors the sites receive on a daily basis. From those figures they ask participants to predict how much growth in traffic the sites will receive.

Participants have the chance to win cash prizes for registering and for taking part in the research.

Currently the Web of Misery has over 480 members who are participating in making predictions.

"People are pretty bullish on bad economy, they are forecasting that these sites are going to grow significantly in terms of traffic over the next quarter and some sites are expected to grow even faster," said Hoffman.