  Positive and negative relations play an essential role in human behavior and
shape the communities we live in. Despite their importance, data about signed
relations is rare and commonly gathered through surveys. Interaction data is
more abundant, for instance, in the form of proximity or communication data. So
far, though, it could not be utilized to detect signed relations. In this
paper, we show how the underlying signed relations can be extracted with such
data. Employing a statistical network approach, we construct networks of signed
relations in four communities. We then show that these relations correspond to
the ones reported in surveys. Additionally, the inferred relations allow us to
study the homophily of individuals with respect to gender, religious beliefs,
and financial backgrounds. We evaluate the importance of triads in the signed
network to study group cohesion.
