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Summary 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.

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We developed an on-line intelligent argumentation system which facilitates stakeholders in exchanging dialogues. It provides decision support by capturing stakeholders' rationale through arguments. As part of the argumentation process, stakeholders tend to both polarise their opinions and form polarisation groups. The challenging issue of assessing argumentation polarisation had not been addressed in argumentation systems until recently. Arvapally, Liu, and Jiang [(2012), 'Identification of Faction Groups and Leaders in Web-Based Intelligent Argumentation System for Collaborative Decision Support', in Proceedings of International Conference on Collaborative Technologies and Systems] earlier developed a method to identify polarisation groups. These groups, however, tend to overlap to a certain degree; each stakeholder may be a member of multiple polarisation groups to varied degrees. Quantifying stakeholders' membership in multiple polarisation groups is an important issue in the argumentation for collaborative decision-making, which is not addressed earlier. We present a novel approach using fuzzy clustering algorithm to address this issue in this article. The method is evaluated using data sets produced from the discussions of 24 stakeholders. Experimental results indicate that our method is effective for both identifying polarisation groups and quantifying stakeholders' degree of membership in each polarisation group.