Sociology in the age of big data

Course content

Because of the digitization of society, sociologists increasingly gain access to new types of so-called “Big (Digital-Trace) Data”. Several prominent sociologists claim that these data will revolutionize sociology. Google Search, for instance, allows us to investigate secret desires that people would probably not even tell their partners and best friends about. Twitter and Reddit offer insights into prejudice and hate against minorities that were only voiced at secret Ku-Klux-Klan meetings in earlier times. Dating sites allow new insights into partnership preferences.

 

In this course we will revisit several classical sociological topics and learn how studies using Big (Digital-Trace) Data have provided new insights. We will learn which digital data sources offer great potential for sociological analyses (e.g., Wikipedia, Twitter, Facebook, digitalized parliamentary speeches), and discuss own ideas of how digital trace data could be analysed to answer pressing sociological questions. Yet, we will also discuss the ethnical and methodological challenges and pitfalls of these studies, and in how far they need to be complemented by research based on established qualitative and quantitative methods.

 

Among others, we will discuss the following topics:

  • Discrimination (of women and ethnic minorities).
  • Dating, Partner choice, and friendship formation.
  • Cultural diffusion, globalization, and social change.
  • Political mobilization and polarization.
  • Knowledge and fact making.
Education

Elective Course
 

Course package (MSc 2015):

Welfare, inequality and mobility
Knowledge, organisation and politics
Culture, lifestyle and everyday life

Learning outcome

Knowledge:

  • Which potentials offer Big Digital-Trace Data?
  • Which sources of Big Digital-Trace Data are the most important for sociological research?
  • What are the challenges and pitfalls of analyzing such data?
  • What are they key insights generated in this new field, so far?
  • Substantial and theoretical knowledge about the topics we discuss (e.g., discrimination, cultural diffusion).
     

Skills:

  • Students will be able to envision how Big Digital-Trace Data could be used to investigate their own research questions.
  • Students will be able to explain how Big Digital-Trace Data could be used to gain insights beyond those that classical methods would allow.
     

Competences:

  • Students will increase their analytical, methodological, logical, and creative cognitive capacities, that is, their sociological imagination.
  • Students will be able to assess (i.e., judge the theoretical and methodological quality of) computational social science studies (also beyond the specific topics of this class).
     

Lectures, class discussions, student presentations, a final paper that entails an empirical analysis. Students are expected to contribute actively to discussion of core theoretical-analytical tools as well as the more specific analytical examples and studies

Readings are comprised primarily of peer-reviewed journal articles. The syllabus will consist of roughly 750 pages of reading.

I expect that students have a solid understanding of basic statistics, such as linear regression

Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)

I give structured feedback to student presentations, and the final paper. Moreover, we will systematically use student peer-feedback.

ECTS
7,5 ECTS
Type of assessment
Written assignment
Individual/group.
A written take-home essay is defined as an assignment that addresses one or more questions. The exam is based on the course syllabus, i.e. the literature set by the teacher. The written take-home essay must be no longer than 10 pages. For group assignments, an extra 5 pages is added per additional student. Further details for this exam form can be found in the Curriculum and in the General Guide to Examinations at KUnet.
Marking scale
7-point grading scale
Censorship form
No external censorship
Criteria for exam assessment

Please see the learning outcome

  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 148
  • Exam
  • 30
  • English
  • 206