Course Title: Social Media and Networks Analytics

Part A: Course Overview

Course Title: Social Media and Networks Analytics

Credit Points: 12.00

Important Information:

Please note that this course may have compulsory in-person attendance requirements for some teaching activities.

Please check your Canvas course shell closer to when the course starts to see if this course requires mandatory in-person attendance. The delivery method of the course might have to change quickly in response to changes in the local state/national directive regarding in-person course attendance. 


Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

COSC2671

City Campus

Postgraduate

171H School of Science

Face-to-Face

Sem 1 2018,
Sem 2 2019

COSC2671

City Campus

Postgraduate

175H Computing Technologies

Face-to-Face

Sem 2 2022

Course Coordinator: Dr Jeffrey Chan

Course Coordinator Phone: +61 3 9925 5270

Course Coordinator Email: jeffrey.chan@rmit.edu.au

Course Coordinator Location: 14.08.15

Course Coordinator Availability: By appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

Enforced Pre-requisite Courses: 

Successful completion of:

 

COSC2531 - Programming Fundamentals (Course ID 045682)
OR
COSC2670 / COSC2791 - Practical Data Science with Python (Course ID 051637)
OR
COSC2752 - Programming Fundamentals for Scientists (Course ID 052878)
OR
COSC2820 - Advanced Programming for Data Science (Course ID 054137)

 

Note: it is a condition of enrolment at RMIT that you accept responsibility for ensuring that you have completed the prerequisite/s and agree to concurrently enrol in co-requisite courses before enrolling in a course.

For your information go to RMIT Course Requisites webpage.


Course Description

Social media and networks are prevalent in our lives. Prominent examples include Facebook, Twitter, LinkedIn and Pinterest.   Due to their widespread adaption, 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. As much of social media and networks are unstructured data, the focus will be in analysis of unstructured data and you will learn about: 

  • Social network analytics: much data is relational, allowing many exciting forms of networks analysis. This course will cover topics in social network analysis such as centrality (identifying the important nodes/people in the network), network clustering analysis and 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).
  • Text analytics: data are textual also, e.g., tweets in Twitter. Hence it is important to analyse them from a textual perspective. In this course you will engage with how to apply basic natural language processing (NLP) to extract meaningful document representations, then use them to understand tweets, their authors, and perform sentiment analysis. 
  • Image analytics: images are another significant form of unstructured data in social media, e.g., images in Pinterest. This course will provide an introduction to computer vision and how it represents and classify images and identify objects in them.


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.

 

Responsibility

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 approaches to analyse social media and social networks.
    2. Analyse social networks via discovering communities, identifying important nodes and propagating influence.
    3. Analyse social media by applying Natural Language Processing (NLP) techniques to detect sentiment and discover topics in text.
    4. Analyse social media by applying Computer Vision techniques.
    5. Describe the theoretical concepts underpinning the social media and network analytical approaches.
    6. Synthesis and present insights from the social media and network analysis performed.


Overview of Learning Activities

You will engage with the key concepts in pre-recorded lectures and lectorials, where course material will be presented and the subject matter will be illustrated with demonstrations and examples.

Workshops 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

A total of 120 hours of study is expected during this course, comprising:

Teacher-directed hours (48 hours): pre-recorded lectures, lectorial and workshop sessions. Each week there will be 2 hours of pre-recorded lecture, 1 hour of lectorials and 1 hour of practical work in a computer laboratory. You are encouraged to participate through asking questions, commenting on the material based on your own experiences and by formulating solutions to provided exercises. The workshop sessions will introduce you to the tools and techniques necessary to undertake the assignment work.

Student-directed hours (72 hours): You are expected to be self-directed, studying independently outside class.


Overview of Learning Resources

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

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


Overview of Assessment

This course has no hurdle requirements.

Assessment tasks

Assessment Task 1: Assignment 1
Weighting 25%
This assessment task supports CLOs 1, 3, 6

Assessment Task 2: Assignment 2
Weighting 50%
This assessment task supports CLOs 1, 2, 3, 4, 6

Assessment Task 3: Group Presentation
Weighting 10%
This assessment supports CLO 6

Assessment Task 4: Take Home Exercises
Weighting 15%
This assessment supports CLO 5