Digital Media Analysis (Autumn 2019)

Abstract

The objective of the course is to learn how to analyze quantitative data about digital media, mainly social media. The course is divided into a theoretical part and a practical part. The theoretical section will introduce general concepts in relation to media consumption and social media use. The practical part will introduce data analysis and data visualization techniques using the R language.

Description

The course is important because quantitative data analysis is an essential part of user research and understanding user behavior and it is going to be more and more relevant with Diga Data analytics playing a central role in any type of business intelligence. In the larger context, students need a versatile toolbox for exploring users’ behavior, to develop a representative understanding of users’ practices. This toolbox needs to cover qualitative and quantitative methods for collecting and analyzing data about user behavior and practice. The objective of the course is to learn how to analyse quantitative data about digital and social media.. The students' task is it to work in groups on a self-chosen research question and present their findings. For the exam every student has to individually write a research report that reflects on learned theoretical concepts and findings (about 10 pages). The course is divided into a theoretical part and a methods part. The theoretical section will introduce general concepts in relation to social media. Further, we will look at the changing media landscape and the social context digital media is embedded in. The methods part will introduce data analysis and data visualization techniques. The methods section will mainly introduce quantitative approaches to data analysis and it will use the programming language R for data analysis and visualization.

The course will cover:

1. Digital Media landscape

2. Introduction to R
   

3. Data visualization with R/ggplot
   
   

4. Datasets and data structures

5. Graphs and network structures on social media data
   

6. Network analysis applied to social media data

Formal prerequisites

Enrolled in the Bachelor programme in Digital Design and Interactive Technologies 

Intended learning outcomes

After the course, the student should be able to:

• describe the notion of Digital Media and Social Media within the context of modern societies
• discuss quantitative and qualitative data analysis techniques and methods applied to Social Media
• design and implement a research project on digital media data
• use R to analyze and visualize quantitative data
• discuss ethical implications of working with social media data

Learning activities

10 lectures + 10 exercise sessions with ad-hoc exercises.

4 in-class supervised sessions to develop the final project and the report.

During the course the students will:

Course literature

The course literature is published in the course page in LearnIT.

Ordinary exam

Exam type:
C: Submission of written work, external (7-trinsskala)
Exam variation:
C: Submission of written work
Exam description:

Students will have to write a research report based on the analysis of one of the social media introduced during the lectures. The report will have to provide an analysis of the specific case as well as the visualization of the data.

Time and date