  Migration is a worldwide phenomenon that may generate different reactions in
the population. Attitudes vary from those that support multiculturalism and
communion between locals and foreigners, to contempt and hatred toward
immigrants. Since anti-immigration attitudes are often materialized in acts of
violence and discrimination, it is important to identify factors that
characterize these attitudes. However, doing so is expensive and impractical,
as traditional methods require enormous efforts to collect data. In this paper,
we propose to leverage Twitter to characterize local attitudes toward
immigration, with a case study on Chile, where immigrant population has
drastically increased in recent years. Using semi-supervised topic modeling, we
situated 49K users into a spectrum ranging from in-favor to against
immigration. We characterized both sides of the spectrum in two aspects: the
emotions and lexical categories relevant for each attitude, and the discussion
network structure. We found that the discussion is mostly driven by Haitian
immigration; that there are temporal trends in tendency and polarity of
discussion; and that assortative behavior on the network differs with respect
to attitude. These insights may inform policy makers on how people feel with
respect to migration, with potential implications on communication of policy
and the design of interventions to improve inter-group relations.
