Information dynamics shape the networks of Internet-mediated prostitution

TL;DR

This study analyzes how online interactions influence offline prostitution networks, revealing feedback loops, geographic clustering, and sublinear growth patterns in an Internet-mediated sex trade community over six years.

Contribution

It provides a detailed empirical analysis of online-offline dynamics in prostitution, highlighting feedback effects, growth patterns, and geographic influences in network structure.

Findings

Feedback loop between post grades and success of sex-workers

Sublinear preferential attachment in community member turnover

Geographic clustering with inverse-square law contact distribution

Abstract

Like many other social phenomena, prostitution is increasingly coordinated over the Internet. The online behavior affects the offline activity; the reverse is also true. We investigated the reported sexual contacts between 6,624 anonymous escorts and 10,106 sex-buyers extracted from an online community from its beginning and six years on. These sexual encounters were also graded and categorized (in terms of the type of sexual activities performed) by the buyers. From the temporal, bipartite network of posts, we found a full feedback loop in which high grades on previous posts affect the future commercial success of the sex-worker, and vice versa. We also found a peculiar growth pattern in which the turnover of community members and sex workers causes a sublinear preferential attachment. There is, moreover, a strong geographic influence on network structure-the network is geographically clustered but still close to connected, the contacts consistent with the inverse-square law observed in trading patterns. We also found that the number of sellers scales sublinearly with city size, so this type of prostitution does not, comparatively speaking, benefit much from an increasing concentration of people.