Computational Social Science Research Methods

An 8-week course in computational social science research methods. The course has a total of 16 instruction hours (8 1-hour lectures and 8 1-hour practice seminars). Lectures 1-4 involve an introduction to quantitative thinking and R, intended for students without experience with programming or statistics. Lectures 5-8 build on lectures 1-4 and involve understanding and working with social network datasets and conducting quantitative analyses. Students with experience in R and statistics can jump in from lecture 5. Advised instruction group size: ~25 students. The course covers the following lectures: 1) introduction to data science and quantitative thinking; 2) introduction to statistics; 3) introduction to R; 4) data analysis in R; 5) big data, sentiment analysis and virality; 6) topic modelling and zero shot classification; 7) social network analysis I: introduction, graph centrality, and community detection; 8) social network analysis II: follower-influencer networks, assortativity, and community comparison.