Title: Statistical Analysis of Online News
Speaker: Laurent El Ghaoui, Electrical Engineering and Computer Science Department,
4:15 - 5:15 pm, Packard 101
Thursday, Nov 29, 2007
(Refreshments after the talk)
Each day we are inundated with an avalanche of online news. Yet is is currently hard to obtain a global view of this information. What are the images that various news media project about specific topics, such as global warming, human rights or presidential candidates? How do these images evolve over time? How do they differ across different media sources, scientific or mainstream? What are the dynamics of news events across news networks?
Modern statistical learning and optimization methods are having a great impact in fields where large amounts of data have become recently available, such as biology or finance. With no doubt, such methods can help shed light on the issues above as well, to the benefit of the social scientist or the ordinary citizen. In turn, online news analysis pushes the boundaries of statistics and optimization towards databases, networks, visualization, and calls for a renewed interaction between computer engineering and social sciences.
I will describe a project which aims at providing user-friendly tools for analyzing large amounts of text data residing in online databases, with a focus on online news data and voting records. I will discuss in particular how online learning and sparsity-inducing methods arise as key ingredients, and I will delineate some related fundamental challenges.
Laurent El Ghaoui graduated from Ecole Polytechnique (
Presentation Slides (pdf)