Module Handbook

  • Dynamischer Default-Fachbereich geändert auf INF

Module INF-71-63-M-6

Social Web Mining (M, 4.0 LP)

Module Identification

Module Number Module Name CP (Effort)
INF-71-63-M-6 Social Web Mining 4.0 CP (120 h)


CP, Effort 4.0 CP = 120 h
Position of the semester 1 Sem. in SuSe
Level [6] Master (General)
Language [EN] English
Module Manager
Area of study [INF-KI] Intelligent Systems
Reference course of study [INF-88.79-SG] M.Sc. Computer Science
Livecycle-State [NORM] Active


Type/SWS Course Number Title Choice in
Presence-Time /
SL SL is
required for exa.
PL CP Sem.
2V+1U INF-71-63-K-6
Social Web Mining
P 42 h 78 h
ja PL1 4.0 SuSe
  • About [INF-71-63-K-6]: Title: "Social Web Mining"; Presence-Time: 42 h; Self-Study: 78 h
  • About [INF-71-63-K-6]: The study achievement "[U-Schein] proof of successful participation in the exercise classes (ungraded)" must be obtained.
    • It is a prerequisite for the examination for PL1.

Examination achievement PL1

  • Form of examination: oral examination (20-60 Min.)
  • Examination Frequency: each summer semester
  • Examination number: 67163 ("Social Web Mining")

Evaluation of grades

The grade of the module examination is also the module grade.


  • RESTful APIs
  • RSS and Atom Syndication
  • Web Crawling and Web Scraping
  • Data Mining
  • Text Mining
  • Network Mining

Competencies / intended learning achievements

Upon successful completion of the module, students will be able to
  • select appropriate techniques for data-driven analysis of Social Web content (social networks, interactive web platforms and e-mail)
  • select suitable methods and technologies from the field of data science in a problem-oriented manner,
  • explain advanced technologies of Social Web Mining,
  • relate different methods of Social Web Mining
  • justify the selection of appropriate methods and technologies for the implementation of Social Web Mining
  • analyze the limits of the application of Social Web Mining methods in the application context
  • evaluate the strengths and weaknesses of Social Web Mining methods depending on the specific application context.


  • Russell, Matthew A. Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More. " O'Reilly Media, Inc.", 2013.

Requirements for attendance of the module (informal)


Requirements for attendance of the module (formal)


References to Module / Module Number [INF-71-63-M-6]

Course of Study Section Choice/Obligation
[INF-88.79-SG] M.Sc. Computer Science [Specialisation] Specialization 1 [WP] Compulsory Elective