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General information
Social Media Analytics
Social Media Analytics
MADS-SMA
SocialMedAna-01-MA-M
Prof. Dr. Schwörer, Tillmann (tillmann.schwoerer@haw-kiel.de)
Prof. Dr. Schwörer, Tillmann (tillmann.schwoerer@haw-kiel.de)
Wintersemester 2025/26
1 Semester
In der Regel jedes Semester
Englisch
Curricular relevance (according to examination regulations)
Study Subject Study Specialization Study Focus Module type Semester
M.Sc. - DS - Data Science Pflichtmodul
Qualification outcome
Areas of Competence: Knowledge and Understanding; Use, application and generation of knowledge; Communication and cooperation; Scientific self-understanding / professionalism.
Students know
- the fundamentals of social media analytics
- state-of-the-art concepts and technologies in the field of natural language processing and network analysis
Students are able
- to apply state-of-the art algorithms in the field of NLP and network analysis to solve real-word problems
- to evaluate the usefulness and quality of algorithms and results
- to critically assess the social implications of algorithms and applications
Students are able
- to report and present solutions for practical project tasks
- to leverage the individual skills of all team members
Students
- to work professionally in the field of social media analytics
- to give and accept professional feedback to different topics of social media analytics
- to identify and process relevant scientific literature
Content information
Course contents:

1. Handling Social Media Data
1.1 Data Acquistion: APIs and Web Scraping
1.2 Data Storage: JSON, Document databases, vector stores

2. Social Network Analysis
2.1 Network analysis and visualization

3. Natural Language Processing (NLP)
3.1 Classical NLP
3.1.1 Preprocessing and feature engineering for text data
3.1.2 Training supervised and unsupervised machine learning models for text data
3.1.3 Topic Modelling

3.2 Transformers in NLP
3.2.1 Embeddings
3.2.2 Transformers and Large Language Models
3.2.3 Transfer learning with Encoders
3.2.4 Generative Language Models
3.2.5 Retrieval Augmented Generation

Example Applications:
- Text classification: e.g. Sentiment Prediction, Hate Speech Detection
- Token classification: e.g. Named Entity Recognition
- Information extraction and text summarization
- Lecture Slides
- Jurafsky, D. and Martin, J.H. (2024): Speech and Language Processing. An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, available online: https://web.stanford.edu/~jurafsky/slp3/
- Sarkar, D. (2019): Text Analytics with Python
Teaching formats of the courses
Teaching format SWS
Lehrvortrag + Übung 4
Workload
4 SWS
5,0 Credits
48 Hours
102 Hours
Module Examination
Method of Examination Duration Weighting wird angerechnet gem. § 11 Absatz 2 PVO Graded Remark
Portfolioprüfung 100 %
Miscellaneous
Solid knowledge of Python Programming and Machine Learning
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