Integrating and Interpreting Social Data from Heterogeneous Sources

Social data is now being published at a never seen before scale. The provision of functionalities and features on a wide range of platforms from microblogging services to photo sharing platforms em- powers users to generate content. However, such is the rate of publica- tion, and the wide range of available platforms to facilitate the creation of social data, that interpreting this data is limited. In this paper we present an approach to interlink social data from multiple Social Web platforms by using Semantic Web technologies to achieve a consistent interpretation of the data. We present a web application to demonstrate the e"ectiveness of this approach, using the Cumbrian Floods in the UK as a use-case for anomaly detection within published social data.