  We present a study of the evolution of the political landscape during the
2015 and 2019 presidential elections in Argentina, based on the data obtained
from the micro-blogging platform Twitter. We build a semantic network based on
the hashtags used by all the users following at least one of the main
candidates. With this network we can detect the topics that are discussed in
the society. At a difference with most studies of opinion on social media, we
do not choose the topics a priori, they naturally emerge from the community
structure of the semantic network instead. We assign to each user a dynamical
topic vector which measures the evolution of her/his opinion in this space and
allows us to monitor the similarities and differences among groups of
supporters of different candidates. Our results show that the method is able to
detect the dynamics of formation of opinion on different topics and, in
particular, it can capture the reshaping of the political opinion landscape
which has led to the inversion of result between the two rounds of the 2015
election.
