Collaborative Tagging and Semiotic Dynamics

TL;DR

This paper analyzes collaborative tagging behavior by collecting data, identifying statistical properties, and proposing a stochastic model that captures key user activity patterns with high accuracy, revealing universal behaviors.

Contribution

It introduces a stochastic model incorporating frequency bias and memory effects that accurately explains observed tagging dynamics.

Findings

Model reproduces empirical tag co-occurrence statistics

User activity follows simple universal patterns

Heavy-tailed memory influences tagging behavior

Abstract

Collaborative tagging has been quickly gaining ground because of its ability to recruit the activity of web users into effectively organizing and sharing vast amounts of information. Here we collect data from a popular system and investigate the statistical properties of tag co-occurrence. We introduce a stochastic model of user behavior embodying two main aspects of collaborative tagging: (i) a frequency-bias mechanism related to the idea that users are exposed to each other's tagging activity; (ii) a notion of memory - or aging of resources - in the form of a heavy-tailed access to the past state of the system. Remarkably, our simple modeling is able to account quantitatively for the observed experimental features, with a surprisingly high accuracy. This points in the direction of a universal behavior of users, who - despite the complexity of their own cognitive processes and the uncoordinated and selfish nature of their tagging activity - appear to follow simple activity patterns.