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Opinion surveys can contain closed questions to which respondents can givemultiple answers. We propose to model these data as networks in which verticesare eligible items and arcs are respondents. This representation opens up thepossibility of using complex networks methodologies to retrieve information andmost prominently, the possibility of using clustering/community detectiontechniques to reduce data complexity. We will take advantage of the implicitnull hypothesis of the modularity function, namely, that items are chosenwithout any preferential pairing, to show how the hypothesis can be testedthrough the usual calculation of p-values. We illustrate the methodologyapplying it to Eurobarometer data. There, a question about national concernscan receive up to two selections. We will show that community clustering groupstogether concerns that can be interpreted in consistent way and in generalterms, such as Economy, Security and Welfare issues. Moreover, we will showthat in this way cleavages between social sectors can be determined.

A Network Model for Multiple Selection Questions in Opinion Surveys