  Climate change, vaccination, abortion, Trump: Many topics are surrounded by
fierce controversies. The nature of such heated debates and their elements have
been studied extensively in the social science literature. More recently,
various computational approaches to controversy analysis have appeared, using
new data sources such as Wikipedia, which help us now better understand these
phenomena. However, compared to what social sciences have discovered about such
debates, the existing computational approaches mostly focus on just a few of
the many important aspects around the concept of controversies. In order to
link the two strands, we provide and evaluate here a controversy model that is
both, rooted in the findings of the social science literature and at the same
time strongly linked to computational methods. We show how this model can lead
to computational controversy analytics that have full coverage over all the
crucial aspects that make up a controversy.
