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In this paper we provide a new view of human human interactions considered as temporal annotated sequences of social signals, combining conversational analysis with social sequence analysis techniques. We provide a novel representation of the conversation as a temporal sequence of social signals. Preliminary analyses over this formalization of the data show the presence of a pattern of overlapping topic changes.

Laughter is an important component of social interaction that has attracted interest within conversational analysis. However, it is not universally accepted that laughs have a function in discourse structure. In this study we explore laughter in conversation in relation to topic changes trying to understand whether laughter can be considered as a signal of discourse structure, whether there is a recurrent pattern in laughter distribution with respect to topic changes, and whether laughter has a function in predicting topic changes. In order to answer these general questions, we investigate the laughter-topic change relation from two different points of view (timing and information flow), finding interesting regularities in laughter distributions. Thus, laughter has a function in discourse structure and, although not sufficient to predict a topic change in isolation, it can be an important indicator together with other sources of information.

One of the main challenges of recent years is to create socially intelligent machines: machines able not only to communicate but also to understand social signals and make sense of the various social contexts. In this paper we describe a new annotation method for the analysis of involvement, aiming at exploring the relation between the individual and the social context in terms of perceived involvement, starting from the idea that the group is more than the sum of its parts. We present a description of the annotation method and preliminary results of an analysis of a multi-party casual conversation extracted from a multimedia multimodal corpus. The work aims to explore the mechanisms by which we react to social context so that we can develop automatic dialogue systems that are able to adapt to their environment in a similar way.