MATH 7014 Social Media Intelligence
Credit Points 10
Legacy Code 301116
Coordinator Laurence Park Opens in new window
Description Social Media Intelligence presents the theory and practice of extracting and analysing information from social media networks. The aims are to identify properties of social networks, and to make predictions about future events. Topics included will cover areas such as Graph theory, Game theory and Network dynamics and we will identify how these can be used to model and extract information from Facebook and Twitter.
School Computer, Data & Math Sciences
Discipline Computer Science
Student Contribution Band HECS Band 2 10cp
Level Postgraduate Coursework Level 7 subject
Assumed Knowledge
Basic algebra and computing skills.
Learning Outcomes
On successful completion of this subject, students should be able to:
- Identify and describe properties of social media networks.
- Compute graph statistics from given social media networks.
- Analyse simple games and describe their connection to social media networks.
- Compute and interpret centrality scores over social media networks.
- Generate and identify small world networks.
- Use a computer to assist in the analysis of large scale social networks.
Subject Content
1. Graph theory and social networks
2. Introduction to Game theory
3. Information networks and the Web
4. Network population models
5. Network structural models
Assessment
The following table summarises the standard assessment tasks for this subject. Please note this is a guide only. Assessment tasks are regularly updated, where there is a difference your Learning Guide takes precedence.
| Type | Length | Percent | Threshold | Individual/Group Task |
|---|---|---|---|---|
| Online quizzes | 5 x 30 minutes | 20 | N | Individual |
| Project | 2000 words | 30 | N | Individual |
| Exam | 2 hours | 50 | N | Individual |
Prescribed Texts
- Easley, D. (2010). Networks, crowds, and markets : reasoning about a highly connected world. New York: Cambridge University Press.
Teaching Periods