Deep tags

Toward a quantitative analysis of online pornography

Dados Bibliográficos

ID13086353
AutoresAntoine Mazières (0000-0001-6405-2617, Université Gustave Eiffel, autor correspondente), 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)
Ano2014
Volume1
Fascículo1-2
Páginas80-95
Data de publicação2014-01-02
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoPorn Studies (JOURNAL)
Identificadores do periódicoISSN: 2326-8743 • E-ISSN: 2326-8751
EditoraTaylor & Francis (PUBLISHER • GB)
DOI10.1080/23268743.2014.888214
OpenAlexW2064121658
IdiomaEN
Citações recebidas31
Referências 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