The team led by Professor Carlos Guestrin used the Cascades algorithm to compile a list of blogs that find the biggest news quickly on the Web.

An excerpt from LockerGnome:

“The goal of our system when looking at blogs is to detect the big stories as early on and as close to the source as possible,” Guestrin said. He, Andreas Krause and Jure Leskovec, doctoral students in computer science and machine learning, respectively, analyzed 45,000 blogs (those that actively link to other blogs) to compile the list, checking the time stamps to determine where news items were being posted first.

The algorithm was originally designed to detect the optimal distribution of sensors for early detection of contamination in water supply.

An excerpt from Science Daily:

“Nothing demonstrates the versatility of Carlos’ algorithm better than its ability to solve these two difficult and seemingly different problems,” said Randal E. Bryant, dean of Carnegie Mellon’s School of Computer Science. “It’s a credit to Carlos’ insight and inventiveness, but also a testament to the power of computational thinking. Computer scientists increasingly are developing common methods for solving problems that apply across any number of disciplines.”

At the Cascades Project site, readers get to view the ranking of top 100 news blogs and also a ranking based on the number of posts.