Simulating information creation in social Semantic Web applications

TL;DR

This paper presents a simulation model for social Semantic Web applications to evaluate ranking algorithms and incentive mechanisms, aiming to predict their impact on information creation in social networks.

Contribution

It introduces a novel simulation model based on the actor-concept-instance framework for semantic social networks, enabling quantifiable evaluation of ranking and incentive algorithms.

Findings

Simulation accurately predicts effects of ranking algorithms

Ranking algorithms influence quality and quantity of information creation

Model extends to generic algorithms and incentive mechanisms

Abstract

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.