From Truth Discovery to Trustworthy Opinion Discovery: An Uncertainty-Aware Quantitative Modeling Approach
Mengting Wan*, UC San Diego; Xiangyu Chen, University of Illinois, Urbana-Champaign; Lance Kaplan, U.S. Army Research Laboratory; Jiawei Han, University of Illinois at Urbana-Champaign; Jing Gao, ; Bo Zhao, LinkedIn
In this era of information explosion, conﬂicts are often encountered when information is provided by multiple sources. Traditional truth discovery task aims to identify the truth –the most trustworthy information, from conﬂicting sources in diﬀerent scenarios. In this kind of tasks, truth is regarded as a ﬁxed value or a set of ﬁxed values. However, in a number of real-world cases, objective truth existence cannot be ensured and we can only identify single or multiple reliable facts from opinions. Diﬀerent from traditional truth discovery task, we address this uncertainty and introduce the concept of trustworthy opinion of an entity, treat it as a random variable, and use its distribution to describe consistency or controversy, which is particularly diﬃcult for data which can be numerically measured, i.e. quantitative information. In this study, we focus on the quantitative opinion, propose an uncertainty-aware approach called Kernel Density Estimation from Multiple Sources (KDEm) to estimate its probability distribution, and summarize trustworthy in-formation based on this distribution. Experiments indicate that KDEm not only has outstanding performance on the classical numeric truth discovery task, but also shows good performance on multi-modality detection and anomaly detection in the uncertain-opinion setting.
Filed under: Data Reliability and Truthfulness