Attention Dynamics in Collaborative Knowledge Creation

TL;DR

This study analyzes large-scale online Q&A networks to understand how collaborative knowledge creation evolves, revealing patterns of attention distribution and proposing a model that combines different attachment processes.

Contribution

It introduces a mixing model combining preferential and reversed preferential attachment to accurately simulate knowledge network evolution.

Findings

Networks show reduced degree inequality and assortative mixing.

New questions struggle to integrate into existing knowledge.

The proposed model reproduces observed network patterns.

Abstract

To uncover the mechanisms underlying the collaborative production of knowledge, we investigate a very large online Question and Answer system that includes the question asking and answering activities of millions of users over five years. We created knowledge networks in which nodes are questions and edges are the successive answering activities of users. We find that these networks have two common properties: 1) the mitigation of degree inequality among nodes; and 2) the assortative mixing of nodes. This means that, while the system tends to reduce attention investment on old questions in order to supply sufficient attention to new questions, it is not easy for novel knowledge be integrated into the existing body of knowledge. We propose a mixing model to combine preferential attachment and reversed preferential attachment processes to model the evolution of knowledge networks and successfully reproduce the ob- served patterns. Our mixing model is not only theoretically interesting but also provide insights into the management of online communities.