Information analysis in mobile social networks for added-value services

The emerging evolution of technology has changed the role of mobile phones which apart from being communication devices are also powerful devices for uploading and consuming content. This fact poses new challenges for the mobile industry, which needs to develop and adapt useful and appealing services for the users in order to enhance the role of the mobile phone as a mainstream device. Adopting and using mobile social networks sites and other Web 2.0 services is expected to be inline with such a mobile technology trend. Current mobile web technologies offer a computer-like user-experience since a user can easily generate and share digital content from his/her mobile. However, current services and applications do not include techniques for analyzing this mass user-generated input (e.g. content, annotations), user interactions (e.g. ranking) and social interactions (e.g. relationships). Knowledge extracted from this massive user contribution and interaction can offer personalized added-value services enabling more efficient mobile usage. Our goal is to outline this information analysis gaps in existing services and going one step further to suggest possible solutions. Aiming at social networks we discuss novel methods for analyzing users’ actions and modeling users’ social relationships. The goal from these suggestions is to extract the underlying knowledge from users’ tagging activities, users’ generated content and users’ social relationships within a social network. We present our points with indicative example services.