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 level||Advanced Bachelor/Master, open to PhD staff and professionals
|Session 1||30 June to 14 July 2018|
|Recommended course combination||Session 2: Artificial Intelligence For a Healthy Life, Data Analysis in R|
Session 3: Big Ideas in Computer Science, Big Data Management and Analysis in Linux
|Co-ordinating lecturers||Prof. Peter Groenewegen|
|Other lecturers||Dr Wouter van Atteveldt, Dr Tijs van den Broek, Prof. Peter Groenewegen, , Dr Christine Moser, Dr Kasper Welbers|
|Form(s) of tuition||Lecture, Interactive seminar|
|Form(s) of assessment||TBA|
|Contact hours||45 hours|
We will work in small groups to address specific issues and do some hands-on analysis. The course is organized around three themes in which big data techniques are used to answer social questions or solve social problems:
In the theme module Digital society, you will look at publicly available sources of data on societal and political participation and discourse such as news items and social media messages. Zooming in on a specific theme that has sparked public contention, such as health care and sustainability issues you will use text and network analysis methods to investigate the way public opinion is formed and the interaction between the discourse of leading politicians, journalists, and active citizens.
In the module social recommenders, you will look at the way recommendation engines are shaping society. 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.
In the second week you will work in mini projects to apply insights from the previous week. In addition to projects that apply directly within the areas taught, big data solutions to disaster response is one of the project options.
At the end of the course, you:
Articles announced in the syllabus Suggested reading material: