Course Title: Social Media and Networks Analytics

Part A: Course Overview

Course Title: Social Media and Networks Analytics

Credit Points: 12.00

Course Coordinator: Dr Jeffrey Chan

Course Coordinator Phone: +61 3 9925 5270

Course Coordinator Email:

Course Coordinator Location: 14.08.15

Course Coordinator Availability: By appointment

Pre-requisite Courses and Assumed Knowledge and Capabilities

Enforced Pre-requisite:  Successful completion of course: Practical Data Science (COSC2670).

Course Description

Social media and networks are prevalent in our lives. Prominent examples include Facebook, Twitter, LinkedIn and Pinterest. However, even banks, supermarkets, telecommunication, shopping centres companies have social media and other data platforms, that they are desperately seeking data scientist/analyst to analyse.


In this course you will learn about areas including:

  • Social network analysis: much data is relational, hence can be represented as networks. This course will cover topics in social network analysis like centrality (identifying the important nodes/people in the network), network clustering analysis, influence propagation (if you have to market something, which people in the network do you give samples to so they can spread how good your products are), etc.
  • Social media analytics: data can be both: textual and relational, e.g., Twitter. In this course you will engage with how to apply basic NLP to extract meaningful document representations, then use them to understand Tweets, their authors, and sentiment analysis. Social media and networks evolve quickly with time. Major challenges are how to process and update our analysis (stream processing) and how to detect interesting events and changes (such as customers bad-mouthing a company online because their system broke down) from monitoring social media (event detection).

Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for MC267 Master of Data Science:

Enabling Knowledge

You will gain skills as you apply knowledge with creativity and initiative to new situations. In doing so, you will:

  • Demonstrate mastery of a body of knowledge that includes recent developments in computer science and information technology;
  • Understand and use appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools;
  • Recognise and use research principles and methods applicable to data science.

Critical Analysis

You will learn to accurately and objectively examine, and critically investigate computer science and information technology (IT) concepts, evidence, theories or situations, in particular to:

  • Analyse and model complex requirements and constraints for the purpose of designing and implementing software artefacts and IT systems;
  • Evaluate and compare designs of software artefacts and IT systems on the basis of organisational and user requirements;
  • Bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of statistical problems.

Problem Solving

Your capability to analyse complex problems and synthesise suitable solutions will be extended as you learn to:

  • Design and implement software solutions that accommodate specified requirements and constraints, based on analysis or modelling or requirements specification;
  • Apply an understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.


You will be required to accept responsibility for your own learning and make informed decisions about judging and adopting appropriate behaviour in professional and social situations. This includes accepting the responsibility for independent life-long learning and a high level of accountability. Specifically, you will learn to:

  • Effectively apply relevant standards, ethical considerations, and an understanding of legal and privacy issues to designing software applications and IT systems;
  • Contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions;
  • Reflect on experience and improve your own future practice;
  • Locate and use data and information and evaluate its quality with respect to its authority and relevance.

On completion of this course you should be able to:

  1. Apply data science to analysis of social media and social networks
  2. Analyse social networks to cluster nodes, identify important nodes, and attribute influence propagation
  3. Analyse social media by applying Natural Language Processing (NLP) techniques using stream processing to detect events and changes

Overview of Learning Activities

You will engage with the key concepts in lectures, classes or online, where course material will be presented and the subject matter will be illustrated with demonstrations and examples.

Tutorials, workshops and/or labs and/or group discussions (including online forums) focused on projects and problem solving will provide you with practice in the application of theory and procedures. You will explore concepts with teaching staff and other students, and receive feedback on your progress. You will develop an integrated understanding of the subject matter through private study by working through the course as presented in classes. Comprehensive learning materials will aid you in gaining practice at solving conceptual and technical problems.


Total study hours

This course includes 2 hours per week of lectures and 2 hours per week of tutorial/laboratory classes.

To achieve high levels of academic results you are expected to spend on average an additional 6 hours per week on self-directed independent learning (reading, online activities and assignments).

Overview of Learning Resources

You will make extensive use of computer laboratories and relevant software provided by the School. You will be able to access course information and learning materials through myRMIT and may be provided with copies of additional materials in class or via email.

Lists of relevant reference texts, resources in the library and freely accessible Internet sites will be provided.

Overview of Assessment

This course has no hurdle requirements.


Assessment tasks

Early Assessment Task: Lab exercises

Weighting 10%

This assessment task supports CLOs 1


Assessment Task 2: Social Network Analysis project

Weighting 20%

This assessment task supports CLOs 1 & 2


Assessment Task 3: Social Media Analytics project

Weighting 20%

This assessment task supports CLOs 1 & 3


Assessment 4: Exam

Weighting 50% 

This assessment supports CLOs 1, 2 & 3