The explosive growth of the Internet, social networks, and user-generated content has led to the generation of vast amounts of digital information, commonly referred to as "Big Data." This revolution has unlocked innovative marketing opportunities but also demands advanced tools and methodologies for analysis. Industry leaders and researchers are increasingly relying on cutting-edge technologies to enable efficient decision-making in the Big Data era.

This course is organized into three key perspectives—user, companies, and platform—each explored in a dedicated module. Progress is evaluated through a mix of theoretical and practical assignments, which can be completed remotely.

This course aims to equip students with the skills to leverage big data for customer analytics, contextual marketing, and online communication management. Participants will learn to analyze text from platforms like TikTok, Reddit, and Amazon reviews, extracting insights on moods, emotions, and behavioral patterns. Through practical sessions, the course demonstrates how these modern technologies enhance decision-making and optimize customer experiences in a data-driven environment, with applications including personalized recommendations, reputation management, influencer strategies, and crisis handling in social media.

After the course, you will be able to:

• Analyze large social media datasets effectively.
• Apply SQL for basic data exploration and analysis.
• Utilize unstructured data, such as text, for actionable insights.
• Understand the latest research on social media crises ("shitstorms") and marketing strategies.
• Distinguish between sentiment, feelings, and emotions.
• Propose automated content strategies informed by data-driven insights.

Attendance is compulsory in all course units. To pass the course, you must be present for at least 80% of the time in the units. If you are unable to attend a unit, please inform the course instructor before the unit. The content covered in a missed unit must be made up independently.

Learning methods include classical knowledge transfer, inquiry, and independent development of topics and issues. In addition, practical examples are demonstrated, which the students adopt.

Grading is based on the following components: Practical (practical assignments), theoretical assignments (theoretical assignments), and in-class participation.

Practical Assignments: practical assignments must be completed for each block. For example, students must complete small assignments. This part is to demonstrate the practical skills learned in this course.

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

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

To successfully pass this course, your cumulative points must exceed 60 points.

Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

Please note that the course has a strong analytical focus. Therefore, students are expected to be interested in data analysis and programming. However, prior knowledge in the areas is not required.

I will be happy to answer your questions. So email me or stop by my office by appointment if you want to speak with me. In addition, I will also try to be available after each online session.

The use of AI-based text generation software such as ChatGPT is not allowed. However, you can use AI-based tools for writing support (e.g., Grammarly).

Recommended Literature

• Seraj, Sarah, Kate G. Blackburn, and James W. Pennebaker. "Language left behind on social media exposes the emotional and cognitive costs of a romantic breakup." Proceedings of the National Academy of Sciences 118.7 (2021).
• Vosoughi, Soroush, Deb Roy, and Sinan Aral. "The spread of true and false news online." Science 359.6380 (2018): 1146-1151.
• Kramer, Adam DI, Jamie E. Guillory, and Jeffrey T. Hancock. "Experimental evidence of massive-scale emotional contagion through social networks." Proceedings of the National Academy of Sciences 111.24 (2014): 8788-8790.
• Berger, Jonah, and Katherine L. Milkman. "What makes online content viral?." Journal of marketing research 49.2 (2012): 192-205.
• Resnick, Paul, and Hal R. Varian. "Recommender systems." Communications of the ACM 40.3 (1997): 56-58.
• Stephens-Davidowitz, Seth. Everybody lies: What the internet can tell us about who we really are. Bloomsbury Publishing, 2018.
• Varian, Hal R. "Big data: New tricks for econometrics." Journal of Economic Perspectives 28.2 (2014): 3-28.