Visualizing cultural digital resources using social network analysis

This paper describes the design and implementation of a prototype to extract, collect and visually analyse cultural digital resources using social network analysis empowered with semantic features. An initial experiment involved the collection and visualization of connections between cultural digital resources - and their providers - stored in the platform DiCet (an Italian Living Lab centred on Cultural Heritage and Technology). This step helped to identify the most appropriate relational data model to use for the social network visualization phase. We then run a second experiment using a web application designed to extract relevant data from the platform Europeana.eu. The actors in our two-mode networks are Cultural Heritage Objects (CHOs) shared by institutional and individual providers, such as galleries, museums, individual experts and content aggregators. The links connecting nodes represent the digital resources associated to the CHOs. The application of the prototype offers insights on the most prominent providers, digital resources and cultural objects over time. Through the application of semantic analysis, we were also able to identify the most used words and the related sentiment associated to them.