Deep Tags: Toward a Quantitative Analysis of Online Pornography

Abstract : The development of the web has increased the diversity of pornographic content, and at the same time the rise of online platforms has initiated a new trend of quantitative research that makes possible the analysis of data on an unpreced- ented scale. This paper explores the application of a quantitative approach to publicly available data collected from pornographic websites. Several analyses are applied to these digital traces with a focus on keywords describing videos and their underlying categorization systems. The analysis of a large network of tags shows that the accumulation of categories does not separate scripts from each other, but instead draws a multitude of significant paths between fuzzy categories. The datasets and tools we describe have been made publicly available for further study.
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Antoine Mazieres, Mathieu Trachman, Jean-Philippe Cointet, Baptiste Coulmont, Christophe Prieur. Deep Tags: Toward a Quantitative Analysis of Online Pornography. Porn Studies, Taylor & Francis, 2014, 1 (1), pp.80-95. ⟨10.1080/23268743.2014.888214⟩. ⟨hal-00937745v2⟩

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