This is a  continuous research module for MCS master students, new students for the WS2020/21 are not admitted.  

The seminar will start on the 28th of October at 16:30-18:45 via Webex.   

Learning-forms: self-learning (extensive literature review), lecture presentations with audio, video-conferencing via webex 

Description: The aim of the seminar is to provide an introduction to concepts, procedures, strategies, and execution of quantitative comparative content analysis on European discourse on immigration. Immigration continues to be one of the most prominent political issues in European societies. Yet individual Member States may perceive and deal with migration differently. In fact, public discourse on immigration may depend on contextual environments that affect communication outcomes, e.g., different periods in time, county's migration history, migration policies, or media systems. The main function of mass media is to report on important events and developments in society, subsequently, news coverage on immigration makes an important contribution to the public debate about immigration. Through this mechanism, news content (visibility, attributes, actors) can act as a source of information and social learning in order to enhance citizens' experience with migration issue. 

The goal of this seminar is twofold: (1) Starting with an overview of the characteristics and the process of quantitative media content analysis, we will discuss in detail the structure of and requirements for category systems, procedure for the development of category systems, classification of coding units; we will learn and test coding; review and revise the category system. In the second semester, (2) we will carry out a (semi-automatic) media content analysis, by combining manual and supervised machine learning techniques. We will compare how constructs of social reality of immigration that mass media form differ across different countries (comparative perspective) and time periods (longitudinal perspective) to account for the fact that systems and cultures are not frozen in time.

Learning objectives: (1) be familiar with different quantitative content analysis methods and the research questions that one can answer by implementing them; (2) know the methodological and practical challenges of realizing comparative longitudinal content analysis research.

Prerequisites: (1) basic knowledge of quantitative research methods in communication science; (2) basic knowledge of statistics; (3) this seminar does not require any prior knowledge of supervised machine learning

Grading: presentations 40%, participation 40%, final paper 20%