  Opinion surveys can contain closed questions to which respondents can give
multiple answers. We propose to model these data as networks in which vertices
are eligible items and arcs are respondents. This representation opens up the
possibility of using complex networks methodologies to retrieve information and
most prominently, the possibility of using clustering/community detection
techniques to reduce data complexity. We will take advantage of the implicit
null hypothesis of the modularity function, namely, that items are chosen
without any preferential pairing, to show how the hypothesis can be tested
through the usual calculation of p-values. We illustrate the methodology
applying it to Eurobarometer data. There, a question about national concerns
can receive up to two selections. We will show that community clustering groups
together concerns that can be interpreted in consistent way and in general
terms, such as Economy, Security and Welfare issues. Moreover, we will show
that in this way cleavages between social sectors can be determined.
