An Incremental Truth Inference Approach to Aggregate Crowdsourcing Contributions in Games with a Purpose
Irene Celino, Gloria Re Calegari

TL;DR
This paper presents an incremental truth inference method tailored for aggregating contributions in Games with a Purpose, demonstrating its advantages through experiments on real GWAP data.
Contribution
It introduces a novel incremental truth inference approach specifically designed for GWAPs, with formalization and experimental validation against existing methods.
Findings
Outperforms state-of-the-art approaches in GWAP data aggregation
Effective in handling contributions from game players
Validated on multiple GWAP datasets
Abstract
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Data-Driven Disease Surveillance · Data Stream Mining Techniques
