The aim of this module is to introduce students to the emerging area of “social computing” and Web-based systems. The module will introduce both theory and practice within this area, allowing students to gain hands-on skills along with a theoretical understanding about some of the key themes. Students are expected to utilise social media (such as Blogs and twitter) as part of the course -- to better capture their thinking and learning experience as they progress through the course -- and to interact better with a wider community on the Internet.

This module builds upon research undertaken at Cardiff University and the content will reflect the current research focus.

1. Understand emerging themes in social and web based computing – focusing on current research topics dominant in this area.
2. Use specialist Application Programming Interfaces (APIs) for analysing social media data feeds.
3. Understand the use of graph theory in representing relationships in social networks and distributed systems.
4. Understand and make use of specialist technologies used to harvest, analyse and visualise “social data”.
5. Demonstrate the use of specialist programming environments and tools for managing distributed social data.
6. Appreciate the importance of trust and reputation mechanisms for managing on-line relationships.
7. Develop business models for harvesting, analysing and managing distributed data.
8. Identify and describe emerging technologies and research areas relevant to social and web based computing.

This module will be delivered through a combination of lectures, supervised lab sessions, example classes and tutorials, as appropriate.

Students will learn use of specialist APIs for analyzing social media. They will also be made aware of and use “mashup” tools for combining different types of data feeds.

The module will also provide students with possible research topics they can investigate further on their own. 

The coursework will allow the student to demonstrate their knowledge and practical skills and to apply the principles taught in lectures.

Students will be assessed on their ability to apply some of the concepts learned during the module, and to evaluate research material published in this area.

Assessment comprises individual or group presentations during module contact sessions. Programming and data analytics skills will be required to prepare some of the presentations.

Exam: A written exam (2 h) will test the student's knowledge and understanding as elaborated under the learning outcomes.

The potential for reassessment in this module is a 100% resit examination during the summer.

Graph Theory & Groups

Hubs, Centrality, Connectivity

Virtual communities

Ad hoc and Mesh Networks

Opportunistic networks

Trust and Reputation

Eigentrust + Related models

Dealing with Free Riding and Malicious behaviour

Secure Social Networks – e.g. “Safebook”

Social Clouds

Analysing Social Media

On-line and commercial systems

Analysis mechanisms (bag of words, “n” gram analysis, parts of speech, parsing of Web pages)

strengths and weaknesses of using Natural Language Processing techniques)

Understanding APIs (Facebook, Twitter and FourSquares)

Applying Graph theory

Data Protection Act + On-line data analysis 

Economic Models

Search Engine Optimisation + AdWords

Business Models for Social Media

Technologies & Techniques

Web Services, Cloud Computing and Peer-2-Peer Systems

Multi-Agent systems

Streaming systems

Autonomic Self-Management in Distributed Systems