Identifying outlier opinions in an online intelligent argumentation system

• Main Authors: ARVAPALLY, R., LIU, X., NAH, Fiona Fui-hoon, JIANG, W.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/9553
• Institution: Singapore Management University

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.