An Incremental Truth Inference Approach to Aggregate Crowdsourcing Contributions in Games with a Purpose
This addresses the specific challenge of handling noisy, game-based crowdsourcing data for applications relying on GWAPs, representing an incremental improvement over existing methods.
The paper tackles the problem of aggregating player contributions in Games with a Purpose (GWAPs) by proposing an incremental truth inference approach, showing through experiments on two GWAP datasets that it outperforms state-of-the-art methods.
We introduce our approach for incremental truth inference over the contributions provided by players of Games with a Purpose: we motivate the need for such a method with the specificity of GWAP vs. traditional crowdsourcing; we explain and formalize the proposed process and we explain its positive consequences; finally, we illustrate the results of an experimental comparison with state-of-the-art approaches, performed on data collected through two different GWAPs, thus showing the properties of our proposed framework.