Truth Discovery for Multiple Judgments With Crowdsourced Sparse Data

Crowdsourced data refers to the information contributed by a large number of individuals, which may originate from various sources, including social media, online surveys, crowdsourcing tasks, etc. It is utilized for analysis and research across diverse scenarios. However, due to various subjective and objective factors, including the participants and sensing devices, the quality of the data collected for crowdsourcing tasks could be inconsistent. Therefore, how to filter out reliable information from the inconsistent data is crucial and difficult. Additionally, since participants consider their time and monetary costs, the crowdsourced datasets obtained are typically based on partial event observations, indicating a pronounced sparsity in the data. Current truth discovery methods struggle to adapt to datasets with varying levels of sparsity and lack effectiveness in evaluating and predicting sparse datasets that contain multiple judgments. In this article, we propose an adaptive hypergraph-based expectation-maximization (EM) truth discovery method for crowdsourced datasets with multiple judgments, named MHGEM (short for multidimensional-hypergraph EM). MHGEM leverages hypergraph topological metrics to model sparse datasets, enhancing the assessment of participant reliability and the prediction of truth for observed events. Experiments in both simulated and real-world scenarios demonstrate that MHGEM achieves higher predictive accuracy.