  In micro-blogging platforms, people can connect with others and have
conversations on a wide variety of topics. However, because of homophily and
selective exposure, users tend to connect with like-minded people and only read
agreeable information. Motivated by this scenario, in this paper we study the
diversity of intermediary topics, which are latent topics estimated from user
generated content. These topics can be used as features in recommender systems
aimed at introducing people of diverse political viewpoints. We conducted a
case study on Twitter, considering the debate about a sensitive issue in Chile,
where we quantified homophilic behavior in terms of political discussion and
then we evaluated the diversity of intermediary topics in terms of political
stances of users.
