  Digital traces of conversations in micro-blogging platforms and OSNs provide
information about user opinion with a high degree of resolution. These
information sources can be exploited to under- stand and monitor collective
behaviors. In this work, we focus on polarization classes, i.e., those topics
that require the user to side exclusively with one position. The proposed
method provides an iterative classification of users and keywords: first,
polarized users are identified, then polarized keywords are discovered by
monitoring the activities of previously classified users. This method thus
allows tracking users and topics over time. We report several experiments
conducted on two Twitter datasets during political election time-frames. We
measure the user classification accuracy on a golden set of users, and analyze
the relevance of the extracted keywords for the ongoing political discussion.
