  This paper presents a user modeling pipeline to analyze discussions and
opinions shared on social media regarding polarized political events (e.g.,
public polls). The pipeline follows a four-step methodology. First, social
media posts and users metadata are crawled. Second, a filtering mechanism is
applied to filter spammers and bot users. As a third step, demographics
information is extracted out of the valid users, namely gender, age, ethnicity
and location information. Finally, the political polarity of the users with
respect to the analyzed event is predicted. In the scope of this work, our
proposed pipeline is applied to two referendum scenarios (independence of
Catalonia in Spain and autonomy of Lombardy in Italy) in order to assess the
performance of the approach with respect to the capability of collecting
correct insights on the demographics of social media users and of predicting
the poll results based on the opinions shared by the users. Experiments show
that the method was effective in predicting the political trends for the
Catalonia case, but not for the Lombardy case. Among the various motivations
for this, we noticed that in general Twitter was more representative of the
users opposing the referendum than the ones in favor.
