Self-organization in social tagging systems

TL;DR

This paper models how social tagging systems self-organize through user imitation, revealing a phase transition influenced by user confidence levels that affects tagging activity and matches real data distributions.

Contribution

It introduces a novel model of self-organization in social tagging systems based on user imitation and confidence, highlighting a phase transition phenomenon.

Findings

Low-confidence users promote active tagging through imitation.

High-confidence users lead to inactive tagging and system stagnation.

Model predictions align well with real-world data distributions.

Abstract

Individuals often imitate each other to fall into the typical group, leading to a self-organized state of typical behaviors in a community. In this paper, we model self-organization in social tagging systems and illustrate the underlying interaction and dynamics. Specifically, we introduce a model in which individuals adjust their own tagging tendency to imitate the average tagging tendency. We found that when users are of low confidence, they tend to imitate others and lead to a self-organized state with active tagging. On the other hand, when users are of high confidence and are stubborn for changes, tagging becomes inactive. We observe a phase transition at a critical level of user confidence when the system changes from one regime to the other. The distributions of post length obtained from the model are compared to real data which show good agreements.