Topics: Introductions and course overview. What is Computational Social Science? Good computing practices.
SLIDES (.pdf): Introduction to the course.
Lazer, D., Pentland, A. S., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., … & Jebara, T. (2009). Life in the network: the coming age of computational social science. Science, 323(5915), 721-3.
Golder, S. A., & Macy, M. W. (2014). Digital footprints: Opportunities and challenges for online social research. Annual Review of Sociology, 40(1), 129.
Grimmer, J. (2015). We are all social scientists now: how big data, machine learning, and causal inference work together. PS: Political Science & Politics, 48(1), 80-83.
Nagler, J. (1995). Coding style and good computing practices. PS: Political Science & Politics, 28(3), 488-492.
Topics: Computational social science: research opportunities and challenges. Overview of theories, methods, and data. Ethics of social science research in the digital age. Big data, big bias? Data collection: automated data collection from the web.
SLIDES (.pdf): Computational Social Science - Research opportunities and challenges.
Lazer, D. & and Radford, J. (2017). Data ex Machina: Introduction to Big Data. Annual Review of Sociology.
Chapter 6 of Salganik, M. (2017). Bit by Bit: Social Research in the Digital Age. Princeton, NJ: Princeton University Press. Open review edition.
Kramer, A. D., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences, 111(24), 8788-8790.
Tufekci, Z. (2014). Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls. ICWSM, 14, 505-514.
Topics: Survey research in the digital age. New sampling techniques. Multilevel regression and poststratification.
Berinsky, A. J., Huber, G. A., & Lenz, G. S. (2012). Evaluating online labor markets for experimental research: Amazon.com’s Mechanical Turk. Political Analysis, 20(3), 351-368.
Wang, W., Rothschild, D., Goel, S., & Gelman, A. (2015). Forecasting elections with non-representative polls. International Journal of Forecasting, 31(3), 980-991.
Jäger, K. (2017). The potential of online sampling for studying political activists around the world and across time. Political Analysis, 1-15.
Mullinix, K. J., Leeper, T. J., Druckman, J. N., & Freese, J. (2015). The generalizability of survey experiments. Journal of Experimental Political Science, 2(2), 109-138.
Topics: Large-scale online experiments. Basics of UNIX. Parallel programming with R.
SLIDES (.pdf): Experimental research in the digital age.
Chen, Y., & Konstan, J. (2015). Online field experiments: a selective survey of methods. Journal of the Economic Science Association, 1(1), 29-42.
Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D., Marlow, C., Settle, J. E., & Fowler, J. H. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489(7415), 295-298.
King, G., Pan, J., & Roberts, M. E. (2014). Reverse-engineering censorship in China: Randomized experimentation and participant observation. Science, 345(6199), 1251722.
Broockman, D., & Kalla, J. (2016). Durably reducing transphobia: A field experiment on door-to-door canvassing. Science, 352(6282), 220-224.
Eckles, D., & Bakshy, E. (2017). Bias and high-dimensional adjustment in observational studies of peer effects. arXiv preprint arXiv:1706.04692.
Topics: Introduction to automated text analysis. Sources of textual data. String manipulation in R. Regular expressions. Text processing with quanteda. Dictionary methods.
Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political analysis, 21(3), 267-297.
Benoit, K. et al. (2017) Getting Started with quanteda.
Young, L., & Soroka, S. (2012). Affective news: The automated coding of sentiment in political texts. Political Communication, 29(2), 205-231.
Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of language and social psychology, 29(1), 24-54.
Topics: supervised machine learning applied to text classification. Crowd-sourcing the creation of training datasets.
SLIDES (.pdf): Introduction to supervised learning applied to text classification.
Chapters 1, 3, and 5 of James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. New York: Springer.
Beauchamp, N. (2017). Predicting and Interpolating State‐Level Polls Using Twitter Textual Data. American Journal of Political Science, 61(2), 490-503.
Benoit, K., Conway, D., Lauderdale, B. E., Laver, M., & Mikhaylov, S. (2016). Crowd-sourced text analysis: reproducible and agile production of political data. American Political Science Review, 110(2), 278-295.
Theocharis, Y., Barberá, P., Fazekas, Z., Popa, S. A. and Parnet, O. (2016), A Bad Workman Blames His Tweets: The Consequences of Citizens’ Uncivil Twitter Use When Interacting With Party Candidates. Journal of Communication, 66: 1007–1031.
Topics: Unsupervised machine learning (topic models).
SLIDES (.pdf): Introduction to unsupervised machine learning. Topic models.
Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77-84.
Roberts, M. E., Stewart, B. M., Tingley, D., Lucas, C., Leder‐Luis, J., Gadarian, S. K., … & Rand, D. G. (2014). Structural Topic Models for Open‐Ended Survey Responses. American Journal of Political Science, 58(4), 1064-1082.
Lucas, C., Nielsen, R. A., Roberts, M. E., Stewart, B. M., Storer, A., & Tingley, D. (2015). Computer-assisted text analysis for comparative politics. Political Analysis, 23(2), 254-277.
Gilardi, F., Shipan, C. R., & Wueest, B. (2017). Policy Diffusion: The Issue-Definition Stage. Working paper, University of Zurich.
Topics: Ideological scaling of documents. Event detection. Introduction to word embeddings.
SLIDES (.pdf): Advanced automated text analysis.
Lowe, W. (2008). Understanding wordscores. Political Analysis, 16(4), 356-371.
Beieler, J., Brandt, P. T., Halterman, A., Schrodt, P. A., & Simpson, E. M. (2016). Generating political event data in near real time, in Alvarez, M. (ed.) Computational Social Science. Cambridge: Cambridge University Press. [Full text available through USC libraries]
Skim: Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
Gurciullo, S., & Mikhaylov, S. J. (2016). Detecting Policy Preferences and Dynamics in the UN General Debate with Neural Word Embeddings. Working paper, University College London.
Denny, M. J., & Spirling, A. (2017). Text preprocessing for unsupervised learning: why it matters, when it misleads, and what to do about it. Working paper, New York University.
Topics: Basic concepts in social network analysis. Types of random networks. Strong and weak ties. Network visualization.
SLIDES (.pdf): Introduction to social network analysis.
Sinclair, B. (2016). Network Structure and Social Outcomes: Network Analysis for Social Science, in Alvarez, M. (ed.) Computational Social Science. Cambridge: Cambridge University Press. [Full text available through USC libraries]
Chapters 1, 2, and 3 in Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets. Cambridge University Press.
Steinert-Threlkeld, Z. C. (2017). Longitudinal Network Centrality Using Incomplete Data. Political Analysis, 1-21.
Bisbee, J., & Larson, J. M. (2017). Testing Social Science Network Theories with Online Network Data: An Evaluation of External Validity. American Political Science Review, 1-20.
Topics: Social contagion processes: homophily vs influence. Collecting social media data.
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual review of sociology, 27(1), 415-444.
Christakis, N. A., & Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. New England Journal of Medicine, 357, 370-379.
Centola, D. (2010). The spread of behavior in an online social network experiment. Science, 329 (5996), 1194-1197.
Aral, S., Muchnik, L., & Sundararajan, A. (2009). Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences, 106(51), 21544-21549.
Topics: Latent variable models, with applications to networks. Introduction to exponential random graph models.
SLIDES (.pdf): Latent space network models. Community detection.
Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social networks, 29(2), 173-191.
Treier, S., & Jackman, S. (2008). Democracy as a latent variable. American Journal of Political Science, 52(1), 201-217.
Barberá, P. (2014). Birds of the same feather tweet together: Bayesian ideal point estimation using Twitter data. Political Analysis, 23(1), 76-91.
Cranmer, S. J., Menninga, E. J., & Mucha, P. J. (2015). Kantian fractionalization predicts the conflict propensity of the international system. Proceedings of the National Academy of Sciences, 112(38), 11812-11816.
Topics: Spatial data. Geographical Information Systems (GIS). Spatial autocorrelation. Spatial autoregresion models.
Gleditsch, K. S., & Weidmann, N. B. (2012). Richardson in the information age: Geographic information systems and spatial data in international studies. Annual Review of Political Science, 15, 461-481.
Weidmann, N. B. (2009). Geography as motivation and opportunity: Group concentration and ethnic conflict. Journal of Conflict Resolution, 53(4), 526-543.
Montgomery, J. M., & Nyhan, B. (2017). The Effects of Congressional Staff Networks in the US House of Representatives. The Journal of Politics, 79(3), 745-761.
Topics: Deep learning. Images, audio, and video as data.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Messing, S., Jabon, M., & Plaut, E. (2016). Bias in the Flesh: Skin Complexion and Stereotype Consistency in Political Campaigns. Public Opinion Quarterly, 80(1), 44–65.
Anastasopoulos, L. J., Badani, D., Lee, C., Ginosar, S., & Williams, J. (2017). Photographic home styles in Congress: a computer vision approach. Working paper, UC Berkeley. [see Blackboard for most recent version.]
Dietrich, B. (2017). Social Polarization in the U.S. House of Representatives. Working paper, University of Iowa.
Knox, D. and Lucas, C. (2017). A General Approach to Classifying Mode of Speech: The Speaker-Affect Model for Audio Data. Working paper, MIT.
Student presentations of research projects.
Student presentations of research projects. Course recap.