Social Media Analytics

Course Description

COURSE OBJECTIVE
The student will acquire knowledge of and insight in:
how and where journalists and professional users of social media can
apply data techniques;
how to gathter large quantities of data;
how to analyse data;
how to visualise data in a correct, insightful and attractive way;
the pros and cons of different software programs;
the specific tasks of different experts.

The student will develop skills in:

• finding, selecting and gathering data;
• processing, organising and analysing data in different formats;
• working with numeral data, in particular in Excel;
• working with unstructured, textual data, by using text mining software; visualize results, by using Excel and other software packages.

In addition to that, students will learn how to work in a team, and under time pressure, on a small assignment. The assignment will result
in an oral presenation and a written report.

COURSE CONTENT
In this course, students will get acquainted with social media analytics. Nowadays, data are avalaible in large quantities. Professional users of social media can make use of this. The challenge is to know where to find these data. At the moment you have access to relevant data, the second challenge is how to extract information from it. It is impossible to work through all these data by reading it, because there is simply too much. This means you will have to work through these data, organise and analyse them with the help of computer programs. We will use data visualisation for two reasons: this way of data-analysis will help you discover stories and it will help you tell the reader what is going on. Big data and data visualisation create new possibilies but at the same time it also creates pitfalls.

In this course you will get answers to the following questions:

• What is the relevance of data mining for journalists and professional users of social media?
• How can you get access to the relevant data?
• What are the methods and techniques?
• How do we interpret and visualise results?
• What moral aspects play a role in working with big data?

We will make use of simple techniques as well als more advanced text mining techniques. In addition to that, we will pay attention to
so-called structured data, such as data presented in spreadsheets and databases. Students will also learn how to work with Excel. In weeks 3 and 4, we will mainly work with textual data. In week 5, we focus on reflection and in the last week we will work with visualisation techniques.

We will work with weekly group assignments and reports. The final assignment is an individual assignment and consists of a small data project.

TEACHING METHODS
Lectures, interactive seminars and practical classes, in total 6 hours per week.

TYPE OF ASSESSMENT
Group assignments and an individual assignment. First, the assignments will test your skills in finding, processing, analysing and visualising data. Second, they will assess your ability to tell a story, accompanied by data and appropriate visualisation. The end grade consitss of the average of the group assignments (50%) and the final assignment (50%). Both components have to be graded at least 5.5.