Probabilistic Models for Fine-Grained Truth Discovery from Crowdsourced Data

Abstract

In crowdsourced data aggregation task, there exist conflicts in the answers provided by large numbers of sources on the same set of questions. The most important challenge for this task is to estimate source reliability and select answers that are provided by high-quality sources. Existing work solves this problem by simultaneously estimating sources' reliability and inferring questions' true answers (i.e., the truths). However, these methods assume that a source has the same reliability degree on all the questions, but ignore the fact that sources' reliability may vary significantly among different topics. To capture various expertise levels on different topics, we propose three fine-grained truth discovery models -- parametric probabilistic model (FaitCrowd), non-parametric probabilistic model and topical influence-aware model -- for the task of aggregating conflicting data collected from multiple users/sources. These probabilistic models jointly model the process of generating question content and sources' provided answers to estimate both fine-grained expertise and true answers simultaneously. This leads to a more precise estimation of source reliability. Therefore, theses models demonstrate better ability to obtain true answers for the questions compared with existing approaches.