Visible to the public From Truth Discovery to Trustworthy Opinion Discovery: An Uncertainty-Aware Quantitative Modeling Approach

TitleFrom Truth Discovery to Trustworthy Opinion Discovery: An Uncertainty-Aware Quantitative Modeling Approach
Publication TypeConference Paper
Year of Publication2016
AuthorsWan, Mengting, Chen, Xiangyu, Kaplan, Lance, Han, Jiawei, Gao, Jing, Zhao, Bo
Conference NameProceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherACM
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-4232-2
Keywordscomposability, kernel density estimation, pubcrawl, source reliability, trustworthiness, trustworthy, truth discovery
Abstract

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 information 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.

URLhttp://doi.acm.org/10.1145/2939672.2939837
DOI10.1145/2939672.2939837
Citation Keywan_truth_2016