Course Title: Social Media and Networks Analytics

Part A: Course Overview

Course Title: Social Media and Networks Analytics

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


175H Computing Technologies


Sem 2 2023

Course Coordinator: Dr Jeffrey Chan

Course Coordinator Phone: +61 3 9925 5270

Course Coordinator Email:

Course Coordinator Location: 014.08.015

Course Coordinator Availability: By Appointment

Pre-requisite Courses and Assumed Knowledge and Capabilities


COSC2738 - Practical Data Science
COSC2802 - Programming Bootcamp 2
COSC2815 - Advanced Programming in Python 

Course Description

Social media and networks are prevalent in our lives. Prominent examples include Facebook, Meta, Discord, Twitter, LinkedIn and Pinterest. Due to their widespread adoption, they provide a great source of behavioural, social and opinion information and have spawned a new analytic field and industry in social media and network analytics.   This has benefited users and organisations in a large variety of fields.

In this course, you will learn how to analyse social media and networks, about different types of analysis that are possible and the algorithms and techniques to perform these analyses. In particular, you will learn:

  • Social network analysis: Relational data is a major type of unstructured data that is prevalent in social media and social network analysis focuses on the study of relational data, from the perspective of human/social networks.  This course will cover fundamental topics, including understanding network representations, discovering latent communities and understanding how rumours spread.
  • Textual analysis: Textual data is another major type of common, unstructured data in social media.  This course will cover basic natural language processing and text analysis, from the context of analysing social media.
  • Image analysis: Images are a third form of unstructured data in social media.  In this course, an introduction will be given about how to process images and how they can be applied to social media analysis.

Objectives/Learning Outcomes/Capability Development

The course is a program option course, however, will contribute to following program learning outcomes for:

BP094 Bachelor of Computer Science
BP340 Bachelor of Data Science
BP347 Bachelor of Computer Science (Professional)
BP348 Bachelor of Data Science (Professional)

PLO1: Knowledge - Apply a broad and coherent set of knowledge and skills for developing user-centric computing solutions for contemporary societal challenges.

PLO2: Problem Solving - Apply systematic problem solving and decision-making methodologies to identify, design and implement computing solutions to real world problems, demonstrating the ability to work independently to self-manage processes and projects.

PLO3: Cognitive and Technical Skill - Critically analyse and evaluate user requirements and design systems employing software development tools, techniques, and emerging technologies.

PLO6: Responsibility and Accountability - Demonstrate integrity, ethical conduct, sustainable and culturally inclusive professional standards, including First Nations knowledges and input in designing and implementing computing solutions.

Upon successful completion of this course, you should be able to:

  1. Apply data science to analyse social media and social networks;
  2. Analyse social networks by finding communities, identifying important nodes, and influence propagation;
  3. Identify and apply Natural Language Processing (NLP) techniques to detect sentiment and events in social media networks;
  4. Describe the theoretical concepts behind social media and network analytical approaches;
  5. Analyse and present insights from the social media and network analysis performed.

Overview of Learning Activities

Teacher-guided learning will include recorded lectures to present main concepts, small-class tutorials to reinforce those concepts, and supervised computer laboratory sessions to support programming practice under guidance from an instructor.

Learner-directed hours include time spent reading and studying lecture notes and prescribed text in order to better understand the concepts; working through examples that illustrate those concepts; and performing exercises and assignments designed by the teachers to reinforce concepts and develop practical skills across a variety of problem types.

Overview of Learning Resources

You are encouraged to bring your laptops and use the freely available software to conduct the laboratories.

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 Canvas and the RMIT Student website, and may be provided with copies of additional materials in class or via email. 

A list of recommended learning resources will be provided by your lecturer, including books, journal articles and web resources. You will also be expected to seek further resources relevant to the focus of your own learning:

There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the RMIT Student website.

Overview of Assessment

This course has no hurdle requirements.

Assessment tasks

Assessment Task 1: Weekly Quizzes
Weighting: 10%  
This assessment task supports CLOs 1, 2, 3 & 4

Assessment Task 2: Take-home Assignment 1 – Text analysis
Weighting: 30%
This assessment task supports CLOs 1, 2, 3, 4 & 5

Assessment Task 3: Take-home Assignment 2 – Social network analysis
Weighting: 40%
This assessment supports CLOs 1, 2, 3, 4 & 5

Assessment Task 4: End-of-semester Take-home Exercises
Weighting: 20%
This assessment supports CLOs 1, 2, 3, 4 & 5