During any social interaction, nonverbal social signals convey just as much information as the conversation itself. While transmitting and analysing conversation is quite a common task for machines, the transmission and analysis of social signals is not. The convergence of cloud computing and mobile computing leads to a situation where insight into social systems is possible, thus paving the way for exciting new applications.
Over the last decade, we have witnessed an increasing usage of mobile devices for capturing, analysing, and predicting human behaviour in everyday activities.
Most modern mobile devices are equipped with a plethora of sensors that capture every aspect of the user’s physical context represented by attributes such as time, location, light, sound, weather, temperature, or even physiological state. Combined with social computing applications such as blogs, email, instant messaging, social networking (Facebook, Twitter, Linked, Google+), Wikis, and social bookmarking, computers are able to capture the social context of the users in terms of interpersonal relationships and roles.
These physical and social contexts describe the existence of a relationship between two entities. Although the structure and nature of such relationships can be interpreted as a semantic network that can be used as the basis for understanding the meaning of an interaction, it fails to reflect the dynamics of a relationship over time. The dynamic patterns of interaction are essential in formalized procedures such as workflows, recurring sequences of actions (such as routine tasks), types of motion (such as walking, running and standing), tasks (such as having lunch, washing dishes, and driving) and goals (for example, socializing, hiring, selling, keeping fit or simply having fun).
Matthias Steinbauer, Ismail Khalil, Gabriele Kotsis
The full article was published in ERCIM News Issue 93 / Apr. 2013. The full issue can be downloaded here.
