ITEC304 - Mining Web and Social Media Data

Year

Credit points

Campus offering

Prerequisites

ITEC202 Data Analytics and Visualisation

Teaching organisation

150 hours over a twelve-week semester or equivalent study period

Unit rationale, description and aim

A prominent output of web and social media is the generation of massive amounts of data, which can be utilised for generating exceptional value, e.g. for predicting customer behaviour. Due to the high volume, velocity, and complexity of web and social media, special tools, technologies and methods are required to obtain and analyse this type of data.

In this unit students will learn the methods and software tools for accessing, analysing, and visualising social media data. This unit also explores the benefits and challenges of analysing web and social media data in real-time; and the technologies to analysing social media data for trends and patterns. The aim of this unit is to equip students with knowledge, skills, and tools for accessing, analysing web and social media data in an ethical manner and generating insightful information to support decision making and improve organisational performance.

Learning outcomes

To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.

Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.

Explore the graduate capabilities.

On successful completion of this unit, students should be able to:

LO1 - Identify the implications, benefits, and challenges of applying web and social media data analytics (GA5, GA8).

LO2 - Access web social media data and understand the ethical implications of doing this (GA3, GA5).

LO3 - Apply tools and methods to identify insightful patterns in web and social media data (GA5, GA10).

LO4 - Present arguments supported by data analysis that demonstrates the potential and impact of social media data (GA5, GA8).

Graduate attributes

GA3 - apply ethical perspectives in informed decision making

GA5 - demonstrate values, knowledge, skills and attitudes appropriate to the discipline and/or profession 

GA8 - locate, organise, analyse, synthesise and evaluate information 

GA10 - utilise information and communication and other relevant technologies effectively.

Content

Topics will include:

• Basic concepts in web and social media data mining
• Implications, benefits and ethical/legal/technical challenges of analysing web social media data
• Methods and tools for gathering web and social data
• Methods and tools for identifying trends in social data
• Visualising web and social media data
• Web Analytics

Learning and teaching strategy and rationale

Students should anticipate undertaking 150 hours of study for this unit, including class attendance, readings, online forum participation and assessment preparation.

This unit is offered in different modes to cater to the learning needs and preferences of a range of participants and maximise effective participation for isolated and/or marginalised groups.

Attendance Mode

In attendance mode, students will require face-to-face attendance in blocks of time determined by the School. Students will have face-to-face interactions with lecturer(s) to further their achievement of the learning outcomes. This unit is structured with required upfront preparation before workshops. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for you to prepare and revise.

Online Mode

This unit uses an active learning approach to support students in the exploration of the essential knowledge associated with working with technology. Students can explore the essential knowledge underpinning technological advances and develop knowledge in a series of online interactive lessons and modules. Students are given the opportunity to attend facilitated synchronous online seminar classes with other students and participate in the construction and synthesis of knowledge, while developing their knowledge of working with technology. Students are required to participate in a series of online interactive workshops which include activities, knowledge checks, discussion and interactive sessions. This approach allows flexibility for students and facilitates learning and participation for students with a preference for virtual learning.

Assessment strategy and rationale

Assessment methods incorporate problem-based tasks, case studies, and practical/hands-on tasks that are relevant to real-world needs. The first assessment item consists of several problem-based tasks designed to develop students' web and social media data mining skills and prepare them for the next two assessment tasks. Assessments 2 and 3 allow students to demonstrate the depth of their knowledge and understanding of web and social media data analytics technology concepts and tools. In assessment task 2 students will develop a proposal and plan for using the web and social media data analytics to solve a real-world problem. In assessment 3, students will provide evidence of their data analysis, and report their data findings/insights.

To pass this unit, students are required to achieve an aggregate mark of at least 50%. Marking will be in accordance with a rubric specifically developed to measure the level of achievement of the learning outcomes for each item of assessment. Students will also be awarded a final grade which signifies their overall achievement in the unit.

Overview of assessments

Representative texts and references

Gabor Szabo, Gungor Polatkan, P. Oscar Boykin, Antonios Chalkiopoulos, 2018, Social Media Data Mining and Analytics, Wiley.

Matthew A Russell, 2019, Mining the Social Web, 3e: Data Mining Facebook, Twitter, Linkedin, Instagram, Github, and More, O'Reilly Media, Inc, USA.

Alex Gonçalves, 2017, Social Media Analytics Strategy, Apress.

Jeremy Harris Lipschultz, 2020, Social Media Measurement and Management Entrepreneurial Digital Analytics, Routledge.

Siddhartha Chatterjee, Michal Krystyanczuk, 2017, Python Social Media Analytics, Packt Publishing.

Sharan Kumar Ravindran, 2015, Mastering Social Media Mining with R, Packt Publishing.