Modeling trustworthy opinion using an uncertainty-aware approach

Chen, Xiangyu

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https://hdl.handle.net/2142/90794 Copy

Description

Title

Modeling trustworthy opinion using an uncertainty-aware approach

Author(s)

Chen, Xiangyu

Issue Date

2016-04-20

Director of Research (if dissertation) or Advisor (if thesis)

• Han, Jiawei
• Rosenbaum, Elyse

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• trustworthy opinion
• 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 or categorically measured. In this study, we propose a Trustworthy Opinion Model (TOM) to model its controversy and consistency, which focusing on both quantitative and categorical opinion. The model uses a Kernel Density Estimation based uncertainty-aware approach to estimate its probability distribution, and summarize trustworthy information based on this distribution. Experiments indicate that TOM 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.

Graduation Semester

2016-05

Type of Resource

text

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http://hdl.handle.net/2142/90794

Copyright and License Information

Copyright 2016 Xiangyu Chen

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