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

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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.