The contribute of Social Network Analysis (SNA) for describing and comprehending the structure of the interactions of web groups and communities: some empirical studies

Starting from quantitative data of the exchanges in a web community, the Social Network Analysis (SNA) is a method for investigating some important dimensions of an exchanges network and their participants. This method of analysis may offer two types of data representation related to the researcher's point of view: ego-cantered analysis (focused on the individuals) whole network analysis (focused on the entire network of interaction). The structural SNA indexes generally used in web communities studies are neighbourhood, connectivity, cohesion, structural equivalence, centrality and centralization. The scope of this contribution is to explore the potentiality of SNA indexes and graphic representations for monitoring, supporting and analysing the interactions in a web group or in a web community. The tutor or the coordinator, but also the researcher, may use this type of analysis as a useful tool for monitoring a web community and locating the potential weak or critical zone in group interactions or for supporting adequately the socialization within the members and their participation to common discussions. But the usefulness of SNA has to be considered not only during the active phase of collaborative activity. For example, through the SNA indexes it is possible to evaluate the relevance of the individual for the group interactions and the role of the tutor and effectiveness of its interventions in critical moments of the collaborative activities. A transversal application of SNA permits to compare different web groups or communities while longitudinally this analysis allows investigating the growth of interactions within their members. This contribution aim to show that with the SNA it is possible to overcome the quantitative level of analysis deriving from tracking data collection, through the evaluation of the functioning of a web community and the representation of the relevance of the single members for the network interactions