Detection and application of influence rankings in small group meetings

Abstract

We address the problem of automatically detecting participant's influence levels in meetings. The impact and social psychological background are discussed. The more influential a participant is, the more he or she influences the outcome of a meeting. Experiments on 40 meetings show that application of statistical (both dynamic and static) models while using simply obtainable features results in a best prediction performance of 70.59% when using a static model, a balanced training set, and three discrete classes: high, normal and low. Application of the detected levels are shown in various ways i.e. in a virtual meeting environment as well as in a meeting browser system. Copyright 2006 ACM.

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