Leila Kosseim, associate professor in Concordia's Faculty of Engineering and Computer Science, and a recently-graduated doctoral student, Shamima Mithun, have developed a system called BlogSum that has potentially vast applications. It allows an organization to pose a question and then find out how a large number of people talking online would respond. The system is capable of gauging things like consumer preferences and voter intentions by sorting through websites, examining real-life self-expression and conversation, and producing summaries that focus exclusively on the original question. "Huge quantities of electronic texts have become easily available on the Internet, but people can be overwhelmed, and they need help to find the real content hiding in the mass of information," explains Kosseim, one of the lead researchers at Concordia's Computational Linguistics Laboratory (CLaC lab). Analyzing informally-written language poses unique challenges compared to analyzing, for example, a news article.