An introduction to Computational Social Science using Big Data
Date: 11th December 2015
Time: 10am – 4.30pm
Instructor: Dr Suzy Moat
Fee: £195 (£140 for those from educational and charitable institutions).
The Cathie Marsh Institute (CMIST) offers five free places to research staff and students within the Faculty of Humanities at The University of Manchester and the North West Doctoral Training Centre.
Postgraduate students requesting a free place will be required to provide a letter of support from their supervisor.
Every day we interact with countless technological systems, which support our communication, our transport, our shopping activities, and much more. Through these interactions, we are generating increasing volumes of “big data” and there is scope for measuring human behaviour, captured in a natural setting at an unprecedented speed and scale.
Such data constitute a new opportunity for social science research. To make maximum use of these datasets, researchers must possess a combination of programming skills and statistical analysis skills, alongside subject specific knowledge.
Participants should be interested in learning basic computer programming and statistical analysis skills, and applying these to datasets.
In this course, participants will be given an overview of recent developments in the field of computational social science, to illustrate the possibilities the new data offers.
Participants will be guided through a case study exercise, demonstrating how data on worldwide usage of online resources such as Google and Wikipedia can be linked to collective behaviour in the real world.
The case study exercise will focus on basic programming skills for acquiring, processing, and analysing data, and will provide participants with a framework for further self study following the course.
No previous experience of programming is required. The course is designed for those with a background in the social sciences, who wish to learn basic programming skills to enable them to make the most of the array of new datasets becoming available.
Participants with a background in disciplines such as computer science, mathematics, or physics who are interested in learning more about research in the field of computational social science may also find the course of value.
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., & Van Alstyne, M. (2009). Computational social science. Science, 323, 721–23.
Moat, H. S., Curme, C., Avakian, A., Kenett, D.Y., Stanley, H. E., & Preis, T. (2013). Quantifying Wikipedia usage patterns before stock market moves. Scientific Reports, 3, 1801.
Preis, T., Moat, H. S., Stanley, H. E., & Bishop, S.R. (2012). Quantifying the advantage of looking forward. Scientific Reports, 2, 350.
Preis, T., Moat, H. S., & Stanley, H. E. (2013). Quantifying trading behavior in financial markets using Google Trends. Scientific Reports, 3, 1684.