Rienks, RutgerZhang, DongGatica-Perez, DanielPost, Wilfried2010-02-112010-02-112010-02-11200610.1145/1180995.1181047https://infoscience.epfl.ch/handle/20.500.14299/47215We 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.text::conference output::conference proceedings::conference paper