Quantifying the relationship between specialisation and reputation in an online platform.

Area of Science:

• Computational Social Science
• Network Science
• Sociology

Background:

• Digital platforms utilize reputation systems to shape collective user behavior.
• Decentralized online environments allow for diverse user strategies and behaviors.
• Stack Overflow serves as a long-standing knowledge-sharing platform for analysis.

Purpose of the Study:

• To statistically characterize user behavior on Stack Overflow.
• To understand the interplay between reputation systems and user self-organization.
• To identify emergent user roles (specialists vs. generalists) and their success.

Main Methods:

• Analysis of user-topic interactions over 11 years using bipartite networks.
• Statistical modeling to link emergent behaviors to the platform's reputation system.
• Comparison of findings with user behavior in traditional hierarchical organizations.

Main Results:

• Stack Overflow networks exhibit nested structures, similar to ecological systems.
• Consistent self-organization into user specialists (narrow topic focus) and generalists (broad topic focus).
• Specialization is statistically linked to a higher success rate in providing the best answers.

Conclusions:

• User behavior on decentralized platforms like Stack Overflow naturally diversifies into specialization and generalization.
• The platform's reputation system influences and is influenced by this self-organization.
• Unlike traditional firms, specialization, not generalization, is associated with higher success in this knowledge-sharing context.