In recent years, computational social science has been emerging. Computational social science is an area that collects traces of people's behavior on the Internet ("digital footprint") and analyzes them. In this course, students will learn how to analyze the social survey and the vast amount of data in the digital era.
By the end of this course, students will be able to:
1) understand and practice the social survey methodology in the digital age.
2) analyze data which you collected.
social survey, big data, statistical analysis
✔ Specialist skills | Intercultural skills | ✔ Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Students summarize the textbook on paper and report it. Students discuss based on the paper. At the last class, students must plan a social survey and make a presentation of it.
Course schedule | Required learning | |
---|---|---|
Class 1 | Guidance | Understand what computational social science is. |
Class 2 | Observing behavior | Understand big data and strategy of analyzing them. |
Class 3 | Asking questions | Understand social surveys in digital era. |
Class 4 | Running experiments | Understand experiments of social sciences in digital era. |
Class 5 | Creating mass collaboration | Understand the strategies to develop our research with collaboration. |
Class 6 | Ethics | Understand the ethics of social surveys in digital era. |
Class 7 | Presentation of survey plan | Make a persuasive presentation of survey plan. |
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Salganik, M. J., 2017, Bit by Bit: Social Research in the Digital Age, Princeton University Press.
None required.
a) Evaluation for those who make summary resume:
summary resume: 30%, final presentation: 30%, commitment: 40%
b) Evaluation for those who don't make summary resume:
final presentation: 50%, commitment: 50%
None required.
kkezuka[at]ila.titech.ac.jp