Toward a Local Perspective on Online Collaboration

TL;DR

This paper proposes a local perspective on online collaboration, showing that a member's local network position better predicts their contribution than their global position, with implications for understanding and analyzing large online communities.

Contribution

It introduces a local view of network position in online collaboration, emphasizing local centrality over global metrics, and develops new algorithms for analyzing local network structures.

Findings

Local centrality predicts contribution better than global centrality.

Members' local network position influences their knowledge contribution.

New algorithms enable analysis of local network structures.

Abstract

We study the structural properties of large scale collaboration in online communities of innovation and the role that position in the community plays in determining knowledge contribution. Contrary to previous research, we argue for a more local perspective when examining online collaboration. We demonstrate that a member's centrality and spanning within his/her local neighborhood is a better predictor of contribution than global centrality and spanning within the whole community. We contribute both theoretically and methodologically to research on large scale collaboration. On the theoretical front, a local view of position implies a more confined and local organization of work in online communities than previously thought. From a methodological perspective, evaluating the local structure of large networks involves radically different algorithms that have only recently become feasible with the increase of processing power.