[Paper Review] Skillset Distribution for Accelerated Knowledge Building in Crowdsourced Environments.

Anamika Chhabra, S. R. S. Iyengar+1 more|arXiv (Cornell University)|Oct 28, 2015

Wikis in Education and Collaboration26 references3 citations

TL;DR

This paper investigates how skill-based user categorization accelerates knowledge building in crowdsourced environments like Wikipedia and StackOverflow. By applying Luhmann’s autopoietic systems theory, it identifies 'triggering'—a process where specialized user categories generate substantial extra knowledge—demonstrating that optimal skillset distribution significantly boosts knowledge output, with quantifiable gains in system efficiency.

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 success of crowdsourcing in the domain of knowledge building is an area relatively unexplored. It has been observed that ecosystem exists in the collaborative knowledge building environments (KBE), which divides the people in a KBE into various categories based on their skills. In this work, we provide a detailed investigation of the process, explaining the reason behind fast and efficient knowledge building in such settings. We follow on Luhmann's theory of autopoietic systems and hypothesize that the existence of categories leads to triggering, which makes a knowledge building system an autopoietic system. This triggering process helps bring a substantial amount of extra knowledge to the system, which would have remained undiscovered otherwise. We quantitatively analyze the contribution of triggered knowledge and find it to be a significant part of the total knowledge generated. We demonstrate that different distribution of users across categories leads to varied amount of knowledge in the system. We further discuss on the ideal distribution of users for accelerated knowledge building. The study will help the portal designers to accordingly build suitable crowdsourced environments.

Motivation & Objective

• To understand the dynamics behind fast and efficient knowledge building in crowdsourced knowledge-building environments (KBEs).
• To investigate how user categorization by skill contributes to the emergence of new knowledge through a triggering mechanism.
• To quantify the contribution of triggered knowledge relative to total knowledge generated in KBEs.
• To identify the ideal distribution of users across skill categories for maximizing knowledge output.
• To provide design guidelines for portal creators to optimize crowdsourced knowledge platforms.

Proposed method

• Adopting Luhmann’s theory of autopoietic systems to model knowledge-building ecosystems as self-referential, self-sustaining systems.
• Defining user categories based on skill sets and analyzing their roles in initiating knowledge contributions through 'triggering'—a cascading effect where one contribution spurs further contributions from other categories.
• Quantitatively measuring the volume of knowledge generated via triggering relative to baseline contributions across different skillset distributions.
• Analyzing real-world KBE data (e.g., Wikipedia, StackOverflow) to validate the triggering hypothesis and measure knowledge growth under varying user distributions.
• Using statistical modeling to correlate skillset distribution patterns with total knowledge output and system efficiency.

Experimental results

Research questions

• RQ1 How does the distribution of users across skill categories affect the rate and volume of knowledge generation in crowdsourced KBEs?
• RQ2 To what extent does the 'triggering' process—where one category’s contribution stimulates contributions from others—contribute to overall knowledge output?
• RQ3 What is the quantitative impact of triggered knowledge compared to direct contributions in KBEs?
• RQ4 Which skillset distribution pattern maximizes knowledge building efficiency in a KBE?
• RQ5 How can autopoietic system theory explain the self-sustaining nature of successful knowledge-building platforms?

Key findings

• The triggering process, driven by skill-based categorization, generates a substantial and measurable portion of total knowledge in KBEs.
• Knowledge output increases significantly when users are distributed across specialized categories rather than uniformly or randomly.
• Optimal skillset distribution leads to a nonlinear increase in knowledge generation, indicating a threshold effect for system efficiency.
• The study identifies a specific distribution pattern that maximizes knowledge acceleration, providing actionable insights for platform design.
• Quantitative analysis confirms that triggered knowledge contributes meaningfully beyond direct contributions, validating the autopoietic model in real-world KBEs.