Semantics-enabled social network analysis

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Given the pivotal significance of understanding customers' interests through systematic monitoring and analysis of social media platforms for call centers, this proposed research project will address important research challenges related to social media analytics. Our work will concentrate on formulating a multi-faceted framework that will collectively consider social interactions (network structure of connections), temporal behaviour (chronological order of activities), and semantics of social content (the underlying meaning of user content) in order to perform social media analytics. Our work will consist of the development and/or adaptation of i) graph techniques that analyze the network structure of social networks; ii) Semantic Web technology that would discover and model latent semantics of social media content; and iii) pattern mining algorithms that can point to recurring behavioural and contextual patterns within the social platform. The focus of our research work will be on developing techniques that consider these three aspects at the same time; therefore, viewing the social network graph as an evolving graph with heterogeneous vertices representing individual users, social artefacts such as user generated content, and latent semantic concepts. Our multi-faceted approach towards the problem that simultaneously considers network structure, content semantics and temporal aspects is the strong research focus of this proposal.