Polarization and opinion analysis in an online argumentation system for collaborative decision support

Argumentation is an important process in a collaborative decision making environment. Argumentation from a large number of stakeholders often produces a large argumentation tree. It is challenging to comprehend such an argumentation tree without intelligent analysis tools. Also, limited decision support is provided for its analysis by the existing argumentation systems. In an argumentation process, stakeholders tend to polarize on their opinions, and form polarization groups. Each group is usually led by a group leader. Polarization groups often overlap and a stakeholder is a member of multiple polarization groups. Identifying polarization groups and quantifying a stakeholder\u27s degree of membership in multiple polarization groups helps the decision maker understand both the social dynamics and the post-decision effects on each group. Frameworks are developed in this dissertation to identify both polarization groups and quantify a stakeholder\u27s degree of membership in multiple polarization groups. These tasks are performed by quantifying opinions of stakeholders using argumentation reduction fuzzy inference system and further clustering opinions based on K-means and Fuzzy c-means algorithms. Assessing the collective opinion of the group on individual arguments is also important. This helps stakeholders understand individual arguments from the collective perspective of the group. A framework is developed to derive the collective assessment score of individual arguments in a tree using the argumentation reduction inference system. Further, these arguments are clustered using argument strength and collective assessment score to identify clusters of arguments with collective support and collective attack. Identifying outlier opinions in an argumentation tree helps in understanding opinions that are further away from the mean group opinion in the opinion space. Outlier opinions may exist from two perspectives in argumentation: individual viewpoint and collective viewpoint of the group. A framework is developed in this dissertation to address this challenge from both perspectives. Evaluation of the methods is also presented and it shows that the proposed methods are effective in identifying polarization groups and outlier opinions. The information produced by these methods help decision makers and stakeholders in making more informed decisions --Abstract, pages iii-iv

Fuzzy C-means Clustering Based Polarization Assessment in Intelligent Argumentation System for Collaborative Decision Support

Intelligent argumentation system facilitates stakeholders to exchange rationale of the stakeholders through arguments. In argumentation process, stakeholders tend to polarize on their opinions and form polarization groups. A method [1] was developed earlier to identify polarization groups, however, polarization groups tend to overlap to a certain degree and each stakeholder may be a member of multiple polarization groups to varied degrees. Quantifying stakeholders\u27 membership in multiple polarization groups in argumentation for collaborative decision making is not addressed earlier. We present an approach using fuzzy clustering algorithm to address this issue and evaluate the approach using an argumentation tree built by twenty four stakeholders