Adaptive notifications to support knowledge sharing in virtual communities

Social web-groups where people with common interests and goals communicate, share resources, and construct knowledge, are becoming a major part of today’s organisational practice. Research has shown that appropriate support for effective knowledge sharing tailored to the needs of the community is paramount. This brings a new challenge to user modelling and adaptation, which requires new techniques for gaining sufficient understanding of a virtual community (VC) and identifying areas where the community may need support. The research presented here addresses this challenge presenting a novel computational approach for community-tailored support underpinned by organisational psychology and aimed at facilitating the functioning of the community as a whole (i.e. as an entity). A framework describing how key community processes—transactive memory (TM), shared mental models (SMMs), and cognitive centrality (CCen)—can be utilised to derive knowledge sharing patterns from community log data is described. The framework includes two parts: (i) extraction of a community model that represents the community based on the key processes identified and (ii) identification of knowledge sharing behaviour patterns that are used to generate adaptive notifications. Although the notifications target individual members, they aim to influence individuals’ behaviour in a way that can benefit the functioning of the community as a whole. A validation study has been performed to examine the effect of community-adapted notifications on individual members and on the community as a whole using a close-knit community of researchers sharing references. The study shows that notification messages can improve members’ awareness and perception of how they relate to other members in the community. Interesting observations have been made about the linking between the physical and the VC, and how this may influence members’ awareness and knowledge sharing behaviour. Broader implications for using log data to derive community models based on key community processes and generating community-adapted notifications are discussed

Intelligent support for knowledge sharing in virtual communities

Virtual communities where people with common interests and goals communicate, share resources, and construct knowledge, are currently one of the fastest growing web environments. A common misconception is to believe that a virtual community will be effective when people and technology are present. Appropriate support for the effective functioning of online communities is paramount. In this line, personalisation and adaptation can play a crucial role, as illustrated by recent user modelling approaches that support social web-groups. However, personalisation research has mainly focused on adapting to the needs of individual members, as opposed to supporting communities to function as a whole. In this research, we argue that effective support tailored to virtual communities requires considering the wholeness of the community and facilitating the processes that influence the success of knowledge sharing and collaboration. We are focusing on closely knit communities that operate in the boundaries of organisations or in the educational sector. Following research in organisational psychology, we have identified several processes important for effective team functioning which can be applied to virtual communities and can be examined or facilitated by analysing community log data. Based on the above processes we defined a computational framework that consists of two major parts. The first deals with the extraction of a community model that represents the whole community and the second deals with the application of the model in order to identify what adaptive support is needed and when. The validation of this framework has been done using real virtual community data and the advantages of the adaptive support have been examined based on the changes happened after the interventions in the community combined with user feedback. With this thesis we contribute to the user modelling and adaptive systems research communities with: (a) a novel framework for holistic adaptive support in virtual communities, (b) a mechanism for extracting and maintaining a semantic community model based on the processes identified, and (c) deployment of the community model to identify problems and provide holistic support to a virtual community. We also contribute to the CSCW community with a novel approach in providing semantically enriched community awareness and to the area of social networks with a semantically enriched approach for modeling change patterns in a closely-knit VC.EThOS - Electronic Theses Online ServiceGBUnited Kingdo