  Identifying controversial topics is not only interesting from a social point
of view, it also enables the application of methods to avoid the information
segregation, creating better discussion contexts and reaching agreements in the
best cases. In this paper we develop a systematic method for controversy
detection based primarily on the jargon used by the communities in social
media. Our method dispenses with the use of domain-specific knowledge, is
language-agnostic, efficient and easy to apply. We perform an extensive set of
experiments across many languages, regions and contexts, taking controversial
and non-controversial topics. We find that our vocabulary-based measure
performs better than state of the art measures that are based only on the
community graph structure. Moreover, we shows that it is possible to detect
polarization through text analysis.
