Social interactions in general are multifaceted and there exists an wide set of factors and events that influence them. Hence, interactions as a social phenomena have been studied by researchers in the fields of psychology and social signal processing from different stand points. The common trend in literature is to perform an in-depth study on a particular aspect of social conversations. Contrast to studying a particular aspect of social interactions, in this research, we attempt to comprehensively quantify social interactions with respect to individual experiences in the interaction, particularly focusing on spontaneous interactions which are typically non-task-directed interactions. To achieve this, we design a novel perceived measure, the perceived Conversation Quality, which intends to quantify spontaneous interactions by accounting for several aspects of spontaneous interactions, namely Rapport, Interaction Quality, Inter-personal liking and Free-for-all. Such an attempt to quantify spontaneous interactions is a substantial contribution towards building socially intelligent system to support human-human and human-robot interaction.

To quantitatively study the perceived Conversation Quality, we devised a questionnaire which measures, at the individual- and at the group- level, the perceived Conversation Quality in spontaneous interactions. Ex- isting literature in the field of social signal processing showed that behavioural features such as Turn-Taking and Bodily Coordination features are informative of several subtle social constructs like cohesion, rapport and interest-levels. Drawing inspiration from them, we extract Turn-Taking and Bodily Coordination features to model perceived Conversation Quality. Using a Logistic Regression, optimised using the Stochastic Gradi- ent Descent algorithm, we were able to predict the individual- and the group- level perceived Conversation Quality with a mean AUC of 0.76 (±0.13) and 0.96 (±0.03) respectively. From the experiments performed, we see that the Synchrony and Convergence based bodily coordination features are the best performing feature sets while predicting the individual-level perceived Conversation Quality and, the Turn-Taking based features are the best performing feature sets while predicting the group-level perceived Conversation Quality.

To further study the properties of the perceived Conversation Quality measure, we performed several statistical tests which study the effect of different social factors on the measure. From theses results, we see that the perceived Conversation Quality, both the the individual- and the group- level, decreases with the increase in number of participants in the spontaneous interaction. Moreover, the equal distribution of talk time amongst participants and the duration of their silence periods have a significant effect on the perceived Conversation Quality. Another interesting finding is that successful and unsuccessful interruptions have a positive effect and a negative effect on perceived Conversation Quality respectively. Moreover, the results also show that the time factor revealing bodily coordination features (lagged correlations and convergence) have a significant effect on both the two levels of Conversation Quality, suggesting that the time factor involved in the bodily coordination is more informative than the degree of coordination in itself. An important takeaway from the statistical test and the predictive modeling experiments is that, the two forms of perceived Conversation Quality, the individual- and the group- level, are completely different from one another with little commonality in their respective statistically significant features.