Social Media Analytics (2225.YR.015133.1)

General information






S semester

ECTS Credits:


Teaching Staff:

Group Teacher Department Language
Year 1 Verena Schoenmueller Marketing ENG


No previous knowledge on social media platforms or coding is required to successfully follow the class. However, prior experience in Python can facilitate the coding exercises.

Workload distribution

The workload of this course is distributed among:
- Lectures and discussions in class
- Exercises (in and outside class)
- Readings
- Project

Course Learning Objectives

At the end of the course, students should be able to understand what data social media platforms can offer and generate insights from social media data for managerial decision problems in marketing. More precisely students should be able to
- Have an understanding of the relevant data offered on social media platforms and how to use this data to make data-driven decisions regarding brands, products and consumers
- Collect social media data
- Manipulate and visualize social media data
- Apply basic statistical tools
- Report insights


1. Social media platforms and social media data

- Overview of social media platforms and data they provide to marketing managers to acquire insights for their brands, products and consumers
- An introduction into how social media platforms changed the way how consumers interact with companies and other consumers such as increasing consumer power and lower information asymmetry
- A discussion regarding the paradigm shift from traditional consumer data to social media data available to companies
- What data do social media platforms offer and how can we use this data? What are the key metrics of social media data?
1) Volume
2) Sentiment
3) Content
4) Networks
5) Images

- What can we learn about products/brand from this data?
- What can we learn about consumers from this data?

2. How to access and analyze social media data

- Commercial solutions (e.g., Brandwatch)
- Access and analyze social media data
1) Datasources such as Yelp, Twitter, Facebook, Amazon
2) Access data
3) Overview of how to visualize and analyze social media data

3. Introduction to Python

- Load data
- Basic operations
- Descriptive statistics

4. Access Twitter data, visualization and basic analyses

- Access data from Twitter
- Descriptive Statistics
- Visualize data
- Analyze data

5. Practice visualization and analysis using consumer online reviews

- Introduction to consumer online reviews
- Overview of major platforms
- Findings in academic research
- Visualization and analysis using consumer online reviews

6. Reflections on privacy and ethical concerns in social media data

- Data privacy
- Algorithmic biases
- Echo chambers

Assessment criteria

Participation (20%)
Individual Quizzes (40%)
Project (40%)

Timetable and sections

Group Teacher Department
Year 1 Verena Schoenmueller Marketing

Timetable Year 1

From 2023/4/25 to 2023/6/27:
Each Tuesday from 8:00 to 9:30. (Except: 2023/5/9)
Each Tuesday from 9:45 to 11:15. (Except: 2023/5/9)