Part A: Course Overview

Course Title: Social Media and Networks Analytics

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

COSC3047

City Campus

Undergraduate

175H Computing Technologies

Face-to-Face

 Sem 2 2023,
Sem 2 2024,
Sem 1 2025

Course Coordinator: Lida Rashidi

Course Coordinator Phone: +61 3 9925

Course Coordinator Email: lida.rashidi@rmit.edu.au

Course Coordinator Availability: By Appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

Recommended Prior Study

You should have satisfactorily completed or received credit for the following course/s before you commence this course:

If you have completed prior studies at RMIT or another institution that developed the skills and knowledge covered in the above course/s you may be eligible to apply for credit transfer.

Alternatively, if you have prior relevant work experience that developed the skills and knowledge covered in the above course/s you may be eligible for recognition of prior learning.

Please follow the link for further information on how to apply for credit for prior study or experience.


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. 


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

You will be actively engaged in a range of learning activities such as lectorials, tutorials, practicals, laboratories, seminars, project work, class discussion, individual and group activities. Delivery may be face to face, online or a mix of both. 
 
You are encouraged to be proactive and self-directed in your learning, asking questions of your lecturer and/or peers and seeking out information as required, especially from the numerous sources available through the RMIT library, and through links and material specific to this course that is available through myRMIT Studies Course. 


Overview of Learning Resources

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

RMIT will provide you with resources and tools for learning in this course through myRMIT Studies Course. 
 
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 myRMIT student portal. 


Overview of Assessment

This course has no hurdle requirements.

Assessment tasks

Assessment Task 1: Assignment 1 
Weighting 20% 
This assessment task supports CLOs 1, 3, 5 

Assessment Task 2: Assignment 2 
Weighting 40% 
This assessment task supports CLOs 1, 2, 3, 5 

Assessment Task 3: Timed and Time-tabled Exercises 
Weighting 40% 
This assessment supports CLO 4 

If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.