  It is now commonplace to observe that we are facing a deluge of online
information. Researchers have of course long acknowledged the potential value
of this information since digital traces make it possible to directly observe,
describe and analyze social facts, and above all the co-evolution of ideas and
communities over time. However, most online information is expressed through
text, which means it is not directly usable by machines, since computers
require structured, organized and typed information in order to be able to
manipulate it. Our goal is thus twofold: 1. Provide new natural language
processing techniques aiming at automatically extracting relevant information
from texts, especially in the context of social sciences, and connect these
pieces of information so as to obtain relevant socio-semantic networks; 2.
Provide new ways of exploring these socio-semantic networks, thanks to tools
allowing one to dynamically navigate these networks, de-construct and
re-construct them interactively, from different points of view following the
needs expressed by domain experts.
