Deep tags

Toward a quantitative analysis of online pornography

Datos Bibliográficos

ID13086353
AutoresAntoine Mazières (0000-0001-6405-2617, Université Gustave Eiffel, autor de correspondencia), Mathieu Trachman (Institut national d'études démographiques), Jean-Philippe Cointet (0000-0002-3172-3960, Université Gustave Eiffel), Baptiste Coulmont (0000-0002-3006-975X, Centre de recherches sociologiques et politiques de Paris), Christophe Prieur (0000-0002-4456-2019, Université Paris Cité)
Año2014
Volumen1
Número1-2
Páginas80-95
Fecha de publicación2014-01-02
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaPorn Studies (JOURNAL)
Identificadores de la revistaISSN: 2326-8743 • E-ISSN: 2326-8751
EditorialTaylor & Francis (PUBLISHER • GB)
DOI10.1080/23268743.2014.888214
OpenAlexW2064121658
IdiomaEN
Citas recibidas31
Referencias citadas38

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 unprecedented 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

Categorization · Data science · Focus (optics · Information retrieval · Multitude · Quantitative analysis (chemistry · Scripting language · The Internet · World Wide Web · Computer Science · Cybercrime and Law Enforcement Studies · Sexuality, Behavior, and Technology · Spam and Phishing Detection · Artificial Intelligence