The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for Social Signal Processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially-aware computing.

The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for Social Signal Processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes aset of recommendations for enabling the development of the next generation of socially-aware computing.

In this paper, we present a formal model of group discussion dyanmics. An understanding of the face-to-face communications in a group discussion can provide new clues about how humans collaborate to accomplish complex tasks and how the collaboration protocols can be learned. It can also help us to evaluate and facilitate brainstorming sessions. We will discuss the following three findings about the dynamics: Meetings in different languages and on different topics could follow the same form of dynamics; The functional roles of the meeting participants could be better understood by inspecting not only their individual speaking and activity features but also their interactions with each other; The outcome of a meeting could be predicted by inspecting how its participants interact.

Social Signal Processing (SSP) aims at the analysis of social behaviour in both Human-Human and Human-Computer interactions. SSP revolves around automatic sensing and interpretation of social signals, complex aggregates of nonverbal behaviours through which individuals express their attitudes towards other human (and virtual) participants in the current social context. As such, SSP integrates both engineering (speech analysis, computer vision, etc.) and human sciences (social psychology, anthropology, etc.) as it requires multimodal and multidisciplinary approaches. As of today, SSP is still in its early infancy, but the domain is quickly developing, and a growing number of works is appearing in the literature. This paper provides an introduction to nonverbal behaviour involved in social signals and a survey of the main results obtained so far in SSP. It also outlines possibilities and challenges that SSP is expected to face in the next years if it is to reach its full maturity.

Abstract – Identifying the behavioral patterns in a social network setting is beneficial to understand how people behave in certain application domains. Such patterns can also be utilized to characterize social signals such as social roles from interactions. In this work, we examine how probabilistic context free grammars (PCFGs) can be utilized to model interactions and role taking in a social network. We describe how to automatically build a PCFG given a set of interactions as the training data. Our experiments on the Mission Survival Corpus 1 (MSC-1) dataset show that PCFGs are a concise way of modeling social entity behaviors and are useful in understanding the probability distribution of interactions as well as the behavior types that are observed.