Big Data in Society

Where Social Science Meets Computer Science

More and more data is being collected about us and our activities. Our social media interactions, online search and shopping behaviour, but also offline location tracking and sensor and surveillance networks are all generating large, often real-time data sets. Potentially, these could help answer important questions facing society today. However, processing big data techniques for social problems in a responsible way within ethical boundaries represents an interdisciplinary challenge at the interface of social and computer science. 

Course levelAdvanced Bachelor/Master, open to PhD staff and professionals
Session 14 July to 18 July 2020
Recommended course combinationSession 2: Operations Research: A Mathematical Way to Optimize Your World, Programming in Python
Co-ordinating lecturersProf. Peter Groenewegen
Other lecturersDr Wouter van Atteveldt, Dr Tijs van den Broek, Prof. Peter Groenewegen, Dr Christine Moser, Dr Kasper Welbers
Form(s) of tuitionLecture, Interactive seminar
Form(s) of assessmentPresentation and written report
ECTS3 credits
Contact hours45 hours
Tuition fee€1150, read more about what's included.
Students and professionals in the field of Social Sciences, Computer and Information Science and Humanities. Since some understanding of social scientific approaches is required, you should previously have studied research methods and methodology. If you have doubts about your eligibility for the course, please let us know. Our courses are multi-disciplinary and therefore are open to students and professionals with a wide variety of backgrounds.

We will work in small groups to address specific issues and do some hands-on analysis. The course is organized around two modules in which big data techniques are used to answer social questions or solve social problems: 

In the module big data, you will look at the way such data are shaping society. For example, recommendation algorithms are determining more and more which songs you listen to, which books you buy, and what news you watch. These recommendations are based on your past behavior but also on data about your friends and other users. These algorithms can help you discover new interests and find items form the ‘long tail’, but they can also cause you to become stuck in a filter bubble of similar items. In this module we will look at existing algorithms and write your own algorithm based on publicly available shopping or review data. Other topics covered are introduction in R, machine learning, and automated text analysis.

In the module Digital society, you will look at publicly available data such as news items, social media messages and online reviews. Zooming in on a specific theme that has sparked public contention, for example health care or sustainability issues, you will use network analysis and other methods to further investigate the data from the first module. Zooming out, we will discuss the implications of big data on society. Specifically, we will discuss CSR, ethics and methodological challenges.

Visits to an organization employing data analytical techniques in a social context

At the end of the course, you:

  • Understand and can use big data.
  • Are able to conceptualize the influences and theories surrounding big data.
  • Learned techniques to analyse various kinds of big data.
  • Can trace the impact of big data on social agents.
  • Understand the core legal and ethical issues of using such data for research.
  • Can work with fellow students from different backgrounds.
Reading materials to be announced.
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