Syllabus

Title
1793 Social Media Analytics
Instructors
Dipl.-Ing. Christian Hotz-Behofsits, B.Sc.
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/13/23 to 09/20/23
Registration via LPIS
Notes to the course
Dates
Day Date Time Room
Monday 10/09/23 01:00 PM - 06:00 PM TC.3.08
Friday 10/13/23 09:00 AM - 12:00 PM TC.5.18
Monday 10/16/23 01:00 PM - 06:00 PM TC.3.12
Friday 10/20/23 09:00 AM - 12:00 PM TC.5.18
Monday 10/23/23 01:00 PM - 07:30 PM TC.3.12
Contents

The rapid proliferation of the Internet and related services, especially social networks and user-generated content, has resulted in incredible amounts of digital information. This is better known as "Big Data" and enables new ways of marketing. However, analyzing massive amounts of data requires specialized tools and methods. Industry managers and researchers increasingly turn to technological innovations that make decision-making effective in Big Data.

The course covers three topics. Each of the three topics is covered in a separate block. Your progress will be assessed through theoretical and practical assignments you can complete at home. 

Learning outcomes

The goal of this course is to provide students with an understanding of how big data can be used for customer analytics (e.g., posting behavior), contextual marketing (e.g., creating personalized recommendations), and online communications management (e.g., reputation management, influencer marketing, and social media crises). Participants are shown how to analyze text social media posts in practical sessions. In this context, the language of Twitter, Reddit, and Amazon reviews is recognized, and moods and emotions are extracted. It will also show how these modern technologies can support management decision-making and optimize customer experience in a data-driven world.

After the course, you will be able to:

  • Cite differences between sentiment, feelings, and emotions
  • Analyze larger social media data sets
  • Know how to leverage unstructured data (e.g., text)
  • Be able to use SQL for basic data exploration
  • To know the current state of research on "shitstorms" and social media marketing
  • Know how to suggest automated content based on data

Attendance requirements

You must attend at least 80% of all class sessions to pass the course (whether online or physical). In the case of online sessions, we expect active participation and a camera turned on. Students are allowed to miss a total of two sessions. Missing one session will not require additional direction; on the second missed session or more, written completion of a replacement assignment will be required. In the case of technical difficulties, evidence of the technical problem (e.g., screenshots) is required.

Teaching/learning method(s)

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

Assessment

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

Component

Max. points

In-class participation (quality and frequency is crucial)

10

Practical assignments

45

Theoretical assignments

45

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:

Points

Grade

90-100

1

80-89

2

70-79

3

60-69

4

< 60

5

 

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

Readings

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.

Recommended previous knowledge and skills

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.

Availability of lecturer(s)

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.

Other

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.
Last edited: 2023-06-19



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