Patterns in Online Knowledge Production

This dissertation will look at large-scale collaboration through the lens of online communities, addressing questions concerning how collaborations attract contributions, what behaviors might give rise to the patterns of collaboration that we see, and what properties of the collaborations drive those behaviors.

 

It is understood that collaborations, online and otherwise, must retain users to remain productive. However, before users can be retained, they must be recruited. In the first project, we identify a few necessary properties of the "attraction'' function by constraining the dynamics of an ODE (Ordinary Differential Equation) model. We further explore the distribution of parameters for over 100 communities from the Stack Exchange network using a combination of direct measurement and fitting.

 

Collaborations do not exist in a vacuum — they compete with and share users with other collaborations. To address this, in the second project we construct an agent-based model of a community of online collaborations using a mechanistic approach. We compare our model to data obtained from the Stack Exchange network and produce similar distributional patterns.

 

In the third project, we explore the influence of different assumptions on the outcome distributions of large scale collaboration. We use variance based sensitivity analysis to evaluate the relative importance of 21 parameters in the model. We find that population parameters impact a great many outcomes and even those parameters that usually have a low impact can be important for some outcomes.