Mining social media in extreme events : Lessons learned from the DARPA network challenge

摘要

The DARPA Network Challenge was a nationwide exercise in the use of social media in extreme events. Teams competed to locate ten red weather balloons that DARPA tethered over public locations across the continental United States for seven to ten hours on Saturday, December 5, 2009. The MIT team won the event, finding all ten locations using monetary incentive and a multi-level marketing payout scheme. This paper outlines the methods used by the 10th place iSchools Caucus team, which used a combination approach of recruiting observers and the use of Open Source Intelligence (OSINT) to find six of the ten locations. Twitter feeds and publicly available content on competing team websites were captured. Data from these mechanisms were evaluated for content validity using a combination of secondary observers, evaluation of the reputation of reported observers and confirmation of the true identities and locations of reporting individuals by mining additional data from several social networking sites. These methods may have application in law enforcement, homeland security and extreme events when there is a desire to use humans as soft sensors, but where it is impossible to directly recruit observers or motivate them with financial incentives.