Research Topics in Networks and Text Analysis

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.

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

Knowledge:

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.

Skills:

The candidate

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