Course Title: Social Media and Networks Analytics
Part A: Course Overview
Course Title: Social Media and Networks Analytics
Credit Points: 12.00
171H School of Science
Sem 1 2018,
Sem 2 2019
Course Coordinator: Dr Jeffrey Chan
Course Coordinator Phone: +61 3 9925 5270
Course Coordinator Email: firstname.lastname@example.org
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) or Programming Fundamentals (COSC2531).
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 the RMIT Course Requisites policy can be found at Course requisites – 126.96.36.199: http://www.rmit.edu.au/browse;ID=twx09y07zi1c
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 behaviourial, social and opinion information and have spawned a new analytic field and industry in social media and network analtyics. 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 analysis. In particular, areas you will learn about include:
- Social network analysis: 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).
- Social media 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. Social media and networks also evolve quickly with time, and a major challenge is how to detect interesting events and changes,such as customers bad-mouthing a company online because their system broke down.
Objectives/Learning Outcomes/Capability Development
This course contributes to the following Program Learning Outcomes for MC267 Master of Data Science:
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.
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.
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:
- Apply data science to analyse social media and social networks
- Analyse social networks by finding communities, identifing important nodes, and influence propagation
- Analyse social media by applying Natural Language Processing (NLP) techniques to detect sentiment and events
- Describe the theoretical concepts behind the social media and network analytical approaches
- Synthesise and present insights from the social media and network analysis performed
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
A total of 120 hours of study is expected during this course, comprising:
Teacher-directed hours (48 hours): lectures, tutorials and laboratory sessions. Each week there will be 2 hours of lecture plus 2 hours 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 tutorial/laboratory 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.
Early Assessment Task: Weekly Quizzes
This assessment task supports CLOs 1, 2, 3, 4
Assessment Task 2: Assignments
Assignment 1: 15%
Assignment 2: 20%
This assessment task supports CLOs 1,2,3,4,5
Assessment 3: Exam
This assessment supports CLOs 1, 2, 3, 4, 5