Social computing covers the area where social behaviour and computer science intersect and this chapter will consider the support for all types of social behavior through computing systems across all facets of society. The focus will be on the potential of social science to explain the psychological impacts of this computing support. For example: an academic online community can support students in the development of critical thinking skills; online support groups can provide empathy and advice for the elderly or ill patients, and online gaming communities can support the social needs of adolescents. The application of theoretical frameworks from the social sciences will be evaluated by drawing on research conducted across a variety of social computing environments (e.g. entertainment, commerce, education and health) and with a variety of users (e.g. adolescents, learners, employees, the elderly, and people with illnesses).
Human beings are social beings who learn, play and work in social contexts; as a result people are extremely sensitive to the behaviour of those around them, and their behaviour is ultimately shaped by their social context. For example, during a learning context a tutor will adapt their teaching style dependent on the students’ verbal and non-verbal cues and in an urban context a market seller will adapt prices depending on demand and customer behaviour. People not only adapt their own behaviour but in groups they conform to other’s social behaviour, e.g. people will be drawn to a crowd gathering around a street artist or people will look up to a high building if some of those around them are doing so. In the discipline of Psychology this process is known as social influence and it is a two-way process, with an individual able to influence those around them and those around them are able to influence the behaviour of the individual (Baron, Branscombe and Byrne, 2009). In summary, social information provides a basis for making perceptions or attributions about other people and helps us to decide how to think, feel and behave. An understanding of social influence can be used at many levels, for example for crowd control, to direct a learning experience or to influence shopping habits. In the same way that face-to-face interaction is predictable, in many ways online social behaviour is also predictable. In online systems social information can be provided directly; for example, the number of users who have rated a product / service as helpful or products recommended based on people with a similar purchasing history. Alternatively, social information may be available indirectly, for example the influence of other’s opinions through online group discussion. In both direct and indirect ways, social information that is produced by a group changes individuals’ perceptions and behaviour and ultimately the functioning of the group. Jarron, Favier and Li (2006) recognise the significance of social influence when they discuss the sources of influence for users of social computing in a commerce context. For example, they discuss the increasing importance that people attach to cues from one another, rather than from institutional sources like corporations, media outlets, religions and political parties. They conclude with advice for companies wanting to thrive using social computing, to weave online communities into their services and products and to use employees and partnerships as significant marketers.
Social computing applications support one-to-one, one-to-many and many-to-many interactions and therefore explanations need to cover individual, group and community processes. It is argued here that social science approaches have a great deal to offer in understanding interactions at each of these levels. At the individual level, social science can explain the impact of personality and individual differences on the use of social computing and the impacts of social computing on self-perception and emotion. At the group level, social science can help to understand group decision making and group cohesion. Increasingly, social computing supports the interactions that are carried out by very large groups of people, or communities (e.g. online auctions and Massive Multiuser Online Role Play Games - MMORPGs). In these contexts, social information processing and social network analysis approaches can be used to track and predict interactions and behaviour (often for commercial use such as market research). Although there are a number of approaches focusing on each level, there is less research exploring how social computing is affecting people at all psychological levels together.
A wide-ranging variety of theories from the disciplines of computing and information systems have been used to explain the impacts of the internet. Many of the early computing approaches were developed to address a specific problem or area of computing use. For example, the ‘technology acceptance model’ (Chau, 1996) explains the take-up and initial use of systems and the ‘task-technology fit theory’ (Goodhue, 1998) proposes that if system capabilities match the tasks that users perform, then impacts will be positive and performance enhanced. However, despite the initial usefulness in predicting usability, take-up and performance, many of these approaches were ill-suited to explaining the increasingly interpersonal and intrapersonal nature of online interaction. Therefore, over the last 10 to 15 years, approaches have increasingly drawn on social science concepts which integrate the social and technical. These integrated approaches to understand social computing will now be reviewed in section 1.2, followed by a review of approaches from the social sciences in section 2 which will highlight those theoretical frameworks that can potentially further our understanding of behaviour at all levels (individual, group and community) in social computing.