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.
In a more recent work, Rienks et al. REF addressed the problem of automatically detecting participant's influence levels in meetings using static models (i.e. SVMs) and a dynamic model, the team-player influence model (a dynamic Bayesian network with a two-level structure: the player level and the team level).
Detection and application of influence rankings in small group meetings
ICMI '06
Computer Science Mathematics