To instill in the student the fundamental principles, issues, opportunities and challenges in enabling information to be modelled, published, and reasoned about on the web; to develop an understanding of the means by which data can be integrated and manipulated as a unified body of information; to be cognisant of the value of, and potential applications of user generated content published on the web.
Social Web and Collective Intelligence
Social Media Tools, techniques and applications; Learning from User Interaction; techniques for extracting Intelligence
Language and Metrics of Networks
Describing Networks - Node and graph metrics and their interpretation. Conducting Social Network Research. Different types of network. Preparing and filtering data. Identifying clusters and patterns.
Learning from networks.
The strength of ties. Trust between people. Diffusion. Analysing content and behaviour. Examples to include Twitter, Facebook and Youtube networks.
Visualising and Analysing Networks.
Layouts and Graph attributes. Links, relationships and hierarchies. Community discovery and analysis. Big data - graph filtering and summarisation.
The Semantic Web and Linked Data
Overview of the Semantic Web Technologies: RDF, RDF/S, OWL and SPARQL; Applications. Web of data. Semantic Web Services.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Other Assessment(s) | 50 |
| Formal Examination | 50 |