  Social networks allow people to connect with each other and have
conversations on a wide variety of topics. However, users tend to connect with
like-minded people and read agreeable information, a behavior that leads to
group polarization. Motivated by this scenario, we study how to take advantage
of partial homophily to suggest agreeable content to users authored by people
with opposite views on sensitive issues. We introduce a paradigm to present a
data portrait of users, in which their characterizing topics are visualized and
their corresponding tweets are displayed using an organic design. Among their
tweets we inject recommended tweets from other people considering their views
on sensitive issues in addition to topical relevance, indirectly motivating
connections between dissimilar people. To evaluate our approach, we present a
case study on Twitter about a sensitive topic in Chile, where we estimate user
stances for regular people and find intermediary topics. We then evaluated our
design in a user study. We found that recommending topically relevant content
from authors with opposite views in a baseline interface had a negative
emotional effect. We saw that our organic visualization design reverts that
effect. We also observed significant individual differences linked to
evaluation of recommendations. Our results suggest that organic visualization
may revert the negative effects of providing potentially sensitive content.

Human-Computer Interaction;Social and Information Networks