Simulating information creation in social Semantic Web applications

Appropriate ranking algorithms and incentive mechanisms are essential to the creation of high-quality information by users of a social network. However, evaluating such mechanisms in a quantifiable way is a difficult problem. Studies of live social networks of limited utility, due to the subjective nature of ranking and the lack of experimental control. Simulation provides a valuable alternative: insofar as the simulation resembles the live social network, fielding a new algorithm within a simulated network can predict the effect it will have on the live network. In this paper, we propose a simulation model based on the actor-conceptinstance model of semantic social networks, then we evaluate the model against a number of common ranking algorithms. We observe their effects on information creation in such a network, and we extend our results to the evaluation of generic ranking algorithms and incentive mechanisms.