Identifying Outlier Opinions in an Online Intelligent Argumentation System
Abstract
Online argumentation systems enable stakeholders to post their problems under consideration and solution alternatives and to exchange arguments over the alternatives posted in an argumentation tree. In an argumentation process, stakeholders have their own opinions, which very often contrast and conflict with opinions of others. Some of these opinions may be outliers with respect to the mean group opinion. This paper presents a method for identifying stakeholders with outlier opinions in an argumentation process. It detects outlier opinions on the basis of individual stakeholder's opinions, as well as collective opinions on them from other stakeholders. Decision makers and other participants in an argumentation process therefore have an opportunity to explore the outlier opinions within their groups from both individual and group perspectives. In a large argumentation tree, it is often difficult to identify stakeholders with outlier opinions manually. The system presented in this paper identifies them automatically. Experiments are presented to evaluate the proposed method. Their results show that the method detects outlier opinions in an online argumentation process effectively.
Recommended Citation
R. S. Arvapally et al., "Identifying Outlier Opinions in an Online Intelligent Argumentation System," Concurrency and Computation, vol. 33, no. 8, John Wiley & Sons, Apr 2021.
The definitive version is available at https://doi.org/10.1002/cpe.4107
Department(s)
Computer Science
Second Department
Business and Information Technology
Research Center/Lab(s)
Intelligent Systems Center
Keywords and Phrases
Computer supported cooperative work; Decision making; Decision support systems; Forestry; Online systems; Argumentation; Argumentation systems; Decision makers; Decision supports; Human-centered computing; Opinion detections; Statistics; Computer-supported collaborative work; Outlier opinion detection
International Standard Serial Number (ISSN)
1532-0626; 1532-0634
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2017 John Wiley & Sons, All rights reserved.
Publication Date
25 Apr 2021
Comments
This project was funded by the Intelligent Systems Center and the National University Transportation Center at the Missouri University of Science and Technology.