Research Topics in Networks and Text Analysis

Postgraduate course

Course description

Objectives and Content

The course is concerned with the use of methods for working with text and network data in information science and media studies. Such methods are becoming increasingly important in the social sciences and in industry for the analysis of data from many different sources, including data from social and mainstream media. The course will establish a theoretical understanding of methods in network science and text analysis and work with programs that implement such methods. The course will cover language models, document clustering and comparison, supervised machine learning methods for the analysis of of documents, and network models for the analysis of the relationships between texts and users. The use of such methods as part of research strategies in information science and media studies will be emphasized and the course will provide theoretical and practical basis for working with methods for analyzing text and networks. 

Participants will become familiar with packages for the analysis of text and network data and gain practical experience by performing their own analysis of real-world data.

Learning Outcomes

A student who has completed the course should have the following learning outcomes defined in terms of knowledge, skills and general competence:


The candidate

  • knows various theoretical and practical issues in network theory and text analysis.
  • knows relevant research and trends in network science and text analysis, as well as new research problems.


The candidate

  • can apply the automated techniques to analyze text and network data and to explain their choice of technologies to solve particular problems.

Level of Study


Semester of Instruction

Required Previous Knowledge
Bachelor in Information Science, Media Studies, or equivalent.
Recommended Previous Knowledge
Knowledge of basic statistical concepts.
Access to the Course
Master in information science. The course is open to other master students and to international students with a solid background in information science under application.
Teaching and learning methods
Lectures, work with subject assignment, presentations and discussions
Compulsory Assignments and Attendance

There will be mandatory assignments which must be completed and approved. The approved mandatory assignments cannot be carried over to other semesters.

Mandatory participation: Attendance at 80 % of course sessions is mandatory.

Forms of Assessment

Oral individual exam (40%)

Group based semester project(s) (60%)

The exam assignment will be given in the language of instruction in the course.
The exam answer must be submitted in the same language as the exam assignment.

Grading Scale
The grading system has a descending scale from A to E for passes and F for fail.
Assessment Semester

Assessment in teaching semester

Students with valid absence as defined in the UiB regulations § 5-5 can apply for an extended submission deadline to The application must be submitted before the deadline for submission has expired.

Course Evaluation
All courses are evaluated according to UiB's system for quality assurance of education.