1485 Social Media Analytics
Dipl.-Ing. Christian Hotz-Behofsits, Ph.D.
Weekly hours
Language of instruction
09/17/20 to 09/24/20
Registration via LPIS
Notes to the course
Day Date Time Room
Tuesday 10/13/20 09:00 AM - 11:30 AM Online-Einheit
Tuesday 10/20/20 08:00 AM - 01:30 PM Online-Einheit
Tuesday 10/27/20 09:00 AM - 03:00 PM Online-Einheit
Tuesday 11/03/20 12:30 PM - 06:30 PM Online-Einheit
Friday 11/06/20 09:00 AM - 12:00 PM Online-Einheit
Procedure for the course when limited activity on campus

The course was changed to distance mode. Therefore, there is no plan B.


The rapid dissemination of the Internet and related services, especially social networks and user-generated content, led to incredible amounts of digital information, better known as big data. This, at the same time, resulted in both, a radical change and new opportunities in marketing.

Analysis of huge amounts of data requires special tools and methods. Industry managers and researchers increasingly embrace technological innovations that make decision-making effective in the context of big data.

Each of the three topics is contained in a separate block, which starts with a lecture covering the theoretical foundations, followed by an applied part. Your learning progress is evaluated by means of theoretical and practical assignments, which can be done at home. Furthermore, each student has to elaborate on a topic (e.g., chatbots, filter bubble or fake news), which is assigned during the first online session. 

Although all slides, teaching materials and syllabus are offered in English, the lectures themselves are held exclusively in German. Therefore, basic knowledge of German is required.

Learning outcomes

The aim of this course is to provide students with an understanding of how big data can be used for customer analytics (social media based segmentation or click-stream analysis), contextual marketing (creating personalized recommendations), and online communication management (e.g., reputation management, influencer marketing and social media crisis).

Students will learn how to use modern technologies for purposes like sentiment analysis, social media based segmentation, and data exploration to support managerial decision making and optimize customer satisfaction in a data-driven world.

After the course, you will be able to:

  • Use common social media application interfaces for scraping purposes.
  • Describe the benefits and limitations of sentiment analysis and recommendation engines.
  • Use SQL for basic data exploration.
  • Analysing social media crisis using state-of-the-art text mining technologies.
  • Gain a better understanding of your customers by using text mining tools (e.g.,analyze unstructured feedback, complains or wishes). 
Attendance requirements

The course will be held online. Nevertheless, an online attendance to the distance learning sessions is compulsory. An attendance of less than 80% of all sessions will lead to not passing the course.

Teaching/learning method(s)

The learning methods include classical knowledge transfer and inquiry as well as the independent elaboration of topics and questions. Additionally, practical examples will be demonstrated which will be adopted by the students.


Grading is based on the following components: A topic elaboration, practical and theoretical assignment. All parts can be done from home.


Max. Points

Topic elaboration


Practical assignments


Theoretical assignments



Topic elaboration: During class, each student is assigned a topic (e.g., chatbots, content filter). The student should then carry out research and write an overview of the respective topic. After a first submission, students get feedback, which should be incorporated before a final submission.

Practical Assignments: For each block, practical assignments have to be done. For example, students have to solve small tasks. This part should demonstrate the practical skills learned in this course.

Theoretical Assignments: For each block, students have to answer theoretical questions. 


These grading components will be added to calculate the final grade, which is based on the following grading scheme:

Overall points










< 60



To successfully pass this course, your cumulated points need to exceed 60 points.


Recommended previous knowledge and skills
Please note that the course has a strong analytical focus. Therefore, students are expected to be interested in data analytics, programming and have a good understanding of basic statistical methods gained through the course “Marketing Research”.
Availability of lecturer(s)

I am happy to answer your questions. So feel free tosend me a short email or drop by my office after making an appointment if you would like to talk to me inperson. I will also try to be available after each online sessions.


Recommended reading for the course:

Sentiment/Emotion Analysis

  • Silge, Julia, and David Robinson. Text mining with R: A tidy approach. " O'Reilly Media, Inc.", 2017.
  • Felbo, Bjarke, et al. "Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm." arXiv preprint arXiv:1708.00524 (2017).


Social media crisis

  • Hansen, Nele, Ann-Kristin Kupfer, and Thorsten Hennig-Thurau. "Brand crises in the digital age: The short-and long-term effects of social media firestorms on consumers and brands." International Journal of Research in Marketing 35.4 (2018): 557-574.
  • Borah, Abhishek, and Gerard J. Tellis. "Halo (spillover) effects in social media: do product recalls of one brand hurt or help rival brands?." Journal of Marketing Research 53.2 (2016): 143-160.
  • Hsu, Liwu, and Benjamin Lawrence. "The role of social media and brand equity during a product recall crisis: A shareholder value perspective." International journal of research in Marketing 33.1 (2016): 59-77.


  • Wickham, Hadley, and Garrett Grolemund. R for data science: import, tidy, transform, visualize, and model data. " O'Reilly Media, Inc.", 2016.
  • Ricci, Francesco, Lior Rokach, and Bracha Shapira. "Introduction to recommender systems handbook." Recommender systems handbook. Springer, Boston, MA, 2011. 1-35.
  • Mikolov, Tomas, et al. "Efficient estimation of word representations in vector space." arXiv preprint arXiv:1301.3781 (2013).
  • Wu, Ledell Yu, et al. "Starspace: Embed all the things!." Thirty-Second AAAI Conference on Artificial Intelligence. 2018.


The course slides will be made available prior to class via Learn@WU. Please make sure that you have access to all course materials! If not, please contact me!

Additional (blank) field

This course provides a deeper insight into the following three data-driven contributions to marketing and sales, enabled by social media and modern big data technologies:

Customer Analytics: As a recent study revealed that customer analytics are dominating big data use in sales and marketing (Datameer 2015). Among other things, it aims to improve conversion rates and lower customer acquisition costs. In this course, R (a well-known and free statistical programming language) will be used to analyze click stream data and exploit social media data for market segmentation. To provide an adequate setting, real-world data sets will be investigated in detail.

Contextual Marketing: Social media platforms know a lot about their customers by gathering huge amounts of data. For example, the video streaming service Netflix uses this information in combination with a personalized recommendation engine to reach high customer satisfaction. In this course, a consumer database will be used to build such a recommendation system. The engine will be based on collaborative filtering, a method of making automatic predictions of the interests of users based on users’ preferences.

Online Communication Management: Social networks have revolutionized communication, providing a public channel where customers can contact businesses, organizations or people directly. On the one hand, these channels allow companies to gain a better understanding of the general market and of their own as well as their competitors' customers. On the other hand, they also pose a threat to any company’s reputation, because they may be used for customer attacks. Nowadays bigger companies are prepared for social media crises (also known as “shitstorms”) by using modern detection technologies. In this part of the course, students will learn recent findings in the fields of social media crisis and influencer marketing. Furthermore, they learn how to use R to apply a sentiment analysis or use emojis as noisy labels to detect emotions.

Unit details
Unit Date Contents
1 13.1
  • Introduction to the Course
  • Crash course: Big & Small Data, Basics of Programming, Scraping and Terminology and SQL
2 20.1
  • Customer Analytics in Theory
  • Customer Analytics in Practice
3 27.1
  • Contextual Marketing in Theory
  • Contextual Marketing in Practice
4 3.11
  • Online Communication Management in Theory
  • Online Communication Management in Practice
5 6.11
  • Practitioners talk
Last edited: 2020-09-21