Nowadays, diverse kinds of data with a large volume have become available to researchers. Online digital trace data in particular has a great potential for new approaches to social science questions.
This course focuses on digital trace data extraction and introduces network analysis which are typical analytical tools for digital trace data. In the first block, students will learn how to collect and manage digital trace data by using Python. In the second block, students learn basic network theories and do exercise to analyze exemplary digital trace data using the network-theoretic concepts such as centralities and clustering. In the third block, the course introduces co-occurrence networks and the exponential random graph model (ERGM). At the end of the course, each student submits a short paper which demonstrates network analyses using digital trace data collected for themselves.
Please bring your own notebook. Alternatively, you can use a computer from the laboratory.