The ubiquity of information and large amounts of data nowadays requires the increased ability to analyze and interpret them in a reflective and critical manner. Moreover, any form of data collection, for example the use of machine learning algorithms, is inherently dependent on the impartiality of the data scientist.

This course is designed to sensitize the participants to how cognitive biases - independent of the intentions of the data scientist - can impair critical thinking when dealing with data. The course draws on the psychology of critical thinking and explains the most common forms of cognitive biases. The course shows the implications for the collection, analysis and interpretation of data by analyzing a variety of examples from everyday management, the media or scientific publications – with an emphasis on social media phenomena. As a byproduct of digitalization, consumers increasingly need the ability to critically reflect on content they see online. Many current developments in the area of social media underline this need. For instance, consumers and companies alike are confronted with fake news, fake reviews, content produced by machine learning algorithms, or moral outcry, social firestorms, or cancel culture.

This course is designed to sensitize the participants to how digital content and data analysis might be biased – sometimes even independent of the intentions of the content producer – and can impair critical thinking when dealing with social media data and content.

This project-based applied course will

1. provide the theoretical foundations on critical thinking on the one hand and social media marketing and GenAI on the other hand. The course draws on behavioral economics and the fundamentals of the psychology of critical thinking to give an overview of biases that are especially prevalent in social media consumption. In addition, we will cover the psychology behind virality of content and the what, why, and how of major current approaches, including applications of GenAI in marketing and content creation.

2. apply the theoretical concepts to a relevant and current case in a group project. Stimulated by contemporary trends in social media and GenAI in general, you will in teams of five choose and work on a current case example and prepare both a presentation and a written seminar paper of your case. You will break down the research questions underlying the case based on a literature search, craft a compelling story and create content to tell the story. The question you address should be very specific both with regard to content and format (e.g., you design a format for WU’s Kids University to educate about responsible social media usage; you develop training material to teach companies how to respond to social media firestorms). In a final session, you present your case, followed by a joint discussion. A written seminar paper is handed it after completion of the course.  

The aim of the course is to provide participants with essential thought-provoking impulses for a critical and responsible approach to data analysis, both from a consumer as well as corporate perspective. By the end of the course, participants should be able to

By the end of the course, participants should be able to

• to reflect critically on their own analytical thinking and data handling
• to be able to assess external information and analyses with regard to their credibility and quality
• communicate more effectively in their own argumentation and presentation of results

To this end, the course provides the most important concepts and tools regarding formal (why is something wrong and how to recognize it) and psychological aspects of critical thinking (why do we tend to draw wrong conclusions and how to avoid them):

• Fundamentals of the psychology of critical thinking
• Overview of the most common cognitive biases in data analytics
• Sensitivity for statistical pitfalls in data analysis and the responsible handling of big data and machine learning algorithms
• Overview of proven strategies to neutralize or minimize the negative effects of cognitive bias
• Application of critical data analysis to contemporary social media phenomena
• Tools to improve your own argumentation and presentation of results.

In addition you will have developed your professional skills in developing a synthesis and story from a research question, creative thinking, and working in teams.

Participation in all sessions is mandatory. You may miss up to max. 20% of lecture hours, but please note that content is build-up in a modular way. Thus, absences should be minimized to ensure your optimal learning outcomes.

• Self-study (reading of scientific articles and own researches of articles, blogs, and videos to prepare group project topic)
   
• Lectures and in-class discussions on theoretical foundations
• Team presentation of group project

Your grade will be the sum of the following components (max. 100 points)

• Project presentation: 40 pts
• Written project report: 40 pts
• Peer evaluation of group work: 10 pts
• Participation in class: 10 pts

Participation in class and the project presentation is graded at the individual level, while the written group project is graded at the group level. The peer review is used to assess the individual contribution of each group member to the group project.

Grades are as follows: 90 pts or more: 1 (= excellent), 80 pts or more: 2 (= good), 70 pts or more: 3 (=satisfactory), 60 pts or more: 4 (= sufficient), 59 pts or less: 5 (= fail).

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