Social Media Analytics (INFS7450)

Course overview

The growth of various social media platforms over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. This course integrates social media, network analysis and data mining to provide a convenient and coherent platform for students to understand the basics and potentials of social media analytics. It introduces basic concepts in social media analytics, metrics to characterize networks, models to explain the generation of networks, and methods to analyse networks. The students learn to use software tools to visualize and analyse real-world social network data. The course also introduces a wide variety of advanced topics in social media analytics such as information diffusion, community detection, behaviour analytics, social recommendations and privacy preserving in social media. If you want to share a piece of information or a site on social media, you would like to grab precious attention from other equally eager users of social media; if you are curious to know what is hidden or who is influential in the complex world of social media, you might wonder how one can find this information in big and messy social media; if you hope to serve your customers better in social media, you certainly want to employ effective means to understand them better. These are just some scenarios in which this course can help.

With various social media platforms (Facebook, Twitter, Instagram, Snapchat, etc.), people can share content, opinions, insights, experiences, perspectives, and media themselves and produce many new media. Social networks emerge with the pervasive use of social media. This course focuses on the analysis of large-scale social networks which provide many computational, algorithmic, and modelling challenges. The course will introduce state-of-the-art developments and research on the structure and analysis of such large social networks and on models and algorithms that abstract their basic properties. We will explore how to practically analyse large-scale network data and how to reason about it through models for network structure and content. Both basic concepts and principles of social networks such as small world, random networks, scale-free networks, community structure, laws and distributions and key techniques of social computing such as fast computation of various network centralities, community detection, and propagation of influence,ﾠlink prediction and graph reprensation ﾠlearning will be covered in this course.

Course Changes in Response to Previous Student Feedback

The description of Project 1 has been refined to highlight its connection to real-world applications, and a broader range of datasets have been incorporated. Lectures will feature more examples and visual elements to enhance engagement. Additionally, foundational knowledge will be expanded for the advanced topic “Graph Representation Learning” to support an easier transition from core material.

Course requirements

Assumed background

Familiarity with probability and statistics, linear algebra, algorithms and data structure, good programming skills (sufficient to write a non-trivial computer program);

Basic Knowledge of Data Mining orﾠMachine Learningﾠ

Prerequisites

You'll need to complete the following courses before enrolling in this one:

(COMP3506 or COMP7505) and STAT7003 and (INFS4203 or INFS7203)

Recommended prerequisites

We recommend completing the following courses before enrolling in this one:

(COMP7703 or COMP4702) and INFS7410

Course contact

Course staff

Timetable

The timetable for this course is available on the UQ Public Timetable.

Aims and outcomes

The aim of this course is to provide a comprehensive introduction to Social Media Analytics (SMA). The topics covered include basic graph theories, node centrality measures, models of the small world, six degrees of separation, network measures and inference, power-laws, cascading behaviour in networks, models of network cascades, Influence maximization in networks, finding communities and clusters in networks, link analysis and prediction, and graph representation learning. The goal is to present fundamental concepts and algorithms for each topic, thus providing the students with the necessary background and practical skills for various social applications. The ultimate goal is to sharpen problem-solving and critical thinking skills of our post-graduate students and prepare them with this unique set of expertise for the increasing demand in the IT industry and for in-depth advanced research.ﾠ

Learning outcomes

After successfully completing this course you should be able to:

Assessment

Assessment summary

A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.

Assessment details

Online Quizzes

• Online

Mode

Written

Category

Quiz

Weight

20%

Due date

14/03/2025 4:00 pm

25/03/2025 4:00 pm

1/04/2025 4:00 pm

15/04/2025 4:00 pm

Other conditions

Time limited.

See the conditions definitions

Learning outcomes

L01

Task description

Submission guidelines

This online quizzes can be completed anywhere. Each quiz has a time limit of 120 minutes and will save and submit automatically when the time expires. Once started, it must be completed in one sitting. Do not leave the test before clicking Save and Submit. Each quiz allows two attempts, and the higher mark will be counted.

Deferral or extension

You cannot defer or apply for an extension for this assessment.

No extensions available and 100% Late penalty applied for the online quizzes as results and feedback are released immediately after the due date. To accommodate unforeseen circumstances such as illness, your mark will be based on the best 3 out of 4 submissions, each worth 5%.

Late submission

You will receive a mark of 0 if this assessment is submitted late.

P1: Fast Computation of User Centrality Measures

Mode

Written

Category

Project

Weight

15%

Due date

16/04/2025 4:00 pm

Learning outcomes

L02, L03, L05

Task description

Submission guidelines

Codes and project reports are to be submitted through UQ Blackboard system before the due dates.

Deferral or extension

You may be able to apply for an extension.

The maximum extension allowed is 14 days. Extensions are given in multiples of 24 hours.

This course uses a progressive assessment approach where feedback and/or detailed solutions will be released to students within 21 days.

Late submission

A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.

P2: Link Prediction in Social Networks

Mode

Written

Category

Project

Weight

15%

Due date

27/05/2025 4:00 pm

Learning outcomes

L03, L04, L05

Task description

Submission guidelines

Codes and project reports are to be submitted through UQ Blackboard system before the due dates.

Deferral or extension

You may be able to apply for an extension.

The maximum extension allowed is 14 days. Extensions are given in multiples of 24 hours.

This course uses a progressive assessment approach where feedback and/or detailed solutions will be released to students within 21 days.

Late submission

A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.

Final Exam

• Hurdle
• Identity Verified
• In-person

Mode

Written

Category

Examination

Weight

50%

Due date

End of Semester Exam Period

7/06/2025 - 21/06/2025

Learning outcomes

L01, L02, L03, L04, L05

Task description

Hurdle requirements

Exam details

Submission guidelines

Deferral or extension

You may be able to defer this exam.

Course grading

Full criteria for each grade is available in the Assessment Procedure.

Additional course grading information

Percentages will not be rounded before any grade cut-offs apply. The course coordinator reserves the right to moderate marks.

Supplementary assessment

Supplementary assessment is available for this course.

Additional assessment information

All online quizzes and projects are to be worked on individually and must be your work. You are encouraged to discuss the concepts or ideas behind the solutions, but under no circumstances should you show your code or answers to or allow your code or answers to be seen by another student. You should not look at the code and answers of any other student. You must sufficiently protect all electronic and paper copies of your code. All submitted code will be subject to electronic plagiarism and collusionﾠdetection. Submissions with no academic merit will be awarded a mark of zero.

You must not commit any code written by anyone apart from yourself or the teaching staff. This applies even if you intend to commit again later.

Having Troubles?

If you are having difficulties with any aspect of the course material, you should seek help and speak to the course teaching staff.

If external circumstances are affecting your ability to work on the course, you should seek help as soon as possible. The University and UQ Union have organisations and staff who are able to help; for example, UQ Student Services are able to help with study and exam skills, tertiary learning skills, writing skills, financial assistance, personal issues, and disability services (among other things).

Complaints and criticisms should be directed in the first instance to the course coordinator. If you are not satisfied with the outcome, you may bring the matter to the attention of the School of EECS Director of Teaching and Learning.

Learning resources

You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.

Library resources

Find the required and recommended resources for this course on the UQ Library website.

Learning activities

The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.

Policies and procedures

University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:

• Student Code of Conduct Policy
• Student Integrity and Misconduct Policy and Procedure
• Assessment Procedure
• Examinations Procedure
• Reasonable Adjustments - Students Policy and Procedure

Learn more about UQ policies on my.UQ and the Policy and Procedure Library.

School guidelines

Your school has additional guidelines you'll need to follow for this course:

• EECS School Guidelines for Students