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A novel method for quantitatively measuring social interactions on small temporal and spatial scales on the basis of interaction geometry (reduced to the parameters interpersonal distance and relative body orientation) with the help of infrared (IR) tracking is introduced. The method is intended to be used to establish a probabilistic classifier to identify existing social situations on the basis of measuring the two parameters for pairs of persons through a series of experiments. The classifier can then be used for characterizing the social context (as an evidence for or against established social situations) of users using sensors in mobile devices in view of useful future Mobile Social Networking services. A first experiment is conducted with the method, a number of standard classifiers including a Gaussian Mixture Model are trained and evaluated and the results are discussed.