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Pornography consumption of the Czech population and its relationship to demographic variables: Clustering using artificial intelligence

Publication at Faculty of Science, Central Library of Charles University |
2021

Abstract

Background: In societies with advanced economies and ubiquitous internet access (such as the Czech Republic), anonymous consumption of porno videos (both BDSM and non-BDSM) has become widespread. However, it is neither clear what fraction of consumers in such a society (and its demographic stratification) consume how much, and how this relates to their education level, masturbation frequencies and to what extent it is sex-dependent.

Aim: First, we are interested in associations between various personal attributes (sex, relationship status, education) and sexual attributes (BDSM and non-BDSM porn video consumption as well as masturbation frequencies). We expect the participants to cluster into distinct segments with well-identifiable features.

Second, we suspect that the segments of personal and sexual attributes associate with a demographic parameter, namely the size of the village, town, city in which they live. The analytical tools were by now unavailable due to computational power and availability of neural-network driven Artificial Intellingence based analytical tools.

Results: We find that the participants cluster into 7 segments; none are exclusively of only one sex, but several are overwhelmingly of one sex only. The spectrum of masturbation frequencies is distinctly different in the segments, as are consumption of porno videos of both types.

Education levels and relationship statuses are not good separators. Highly compulsive porno video consumption, together with high masturbation frequencies, characterize two segments, while restraining (conservative) sexual attitudes are characteristic of two others.

Associations with demography are as expected: members with conservative sexual attitudes are primarily female and live in small towns and villages, as do males with highly impulsive sexual attitudes. Conclusion: Modern, advanced statistical methods can identify segments of adults with well-defined sexual attributes and associations with demography that would otherwise remain undetected.