Ideal Composition of a Group for Maximal Knowledge Building in Crowdsourced Environments

TL;DR

This paper investigates how the composition of user groups in crowdsourced knowledge environments affects knowledge building, proposing an algorithm to optimize user mix for maximal knowledge growth.

Contribution

It introduces a hill climbing algorithm to determine the optimal user composition in collaborative knowledge environments based on triggering dynamics.

Findings

Optimal user mix enhances knowledge building efficiency

Algorithm effectively identifies ideal group compositions

Findings applicable to designing better crowdsourced platforms

Abstract

Crowdsourcing has revolutionized the process of knowledge building on the web. Wikipedia and StackOverflow are witness to this uprising development. However, the dynamics behind the process of crowdsourcing in the domain of knowledge building is an area relatively unexplored. It has been observed that an ecosystem exists in the collaborative knowledge building environments (KBE), which puts users of a KBE into various categories based on their expertise. Classical cognitive theories indicate triggering among the knowledge units to be one of the most important reasons behind accelerated knowledge building in collaborative KBEs. We use the concept of ecosystem and the triggering phenomenon to highlight the necessity for the right mix of users in a KBE. We provide a hill climbing based algorithm which gives the ideal mixture of users in a KBE, given the amount of triggering that takes place among the users of various categories. The study will help the portal designers to accordingly build suitable crowdsourced environments.