Note: slides will be added as the course progresses.
Topics: Introductions and course overview. What is Computational Social Science? Good computing practices. Introduction to version control and GitHub
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. Ethics of social science research in the digital age. Big data, big bias?
SLIDES (.pdf): Computational Social Science - Research opportunities and challenges.
Barberá, P. & and Steinert-Threlkeld, Z. (2017). How to Use Social Media Data for Political Science Research. Forthcoming in Curini, L., and Franzese, R. (eds) The SAGE Handbook of Research Methods in Political Science and International Relations, London: Sage, 2019.
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.
Hargittai, E. (2018). Potential Biases in Big Data: Omitted Voices on Social Media. Social Science Computer Review, forthcoming.
Topics: Large-scale online experiments. Data collection: automated data collection from the web.
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.
Munger, K. (2017). Tweetment Effects on the Tweeted: An Experiment to Decrease Online Harassment. Political Behavior, 39(3), 629-649.
Bail et al (2018). Exposure to opposing views on social media can increase political polarization. Proceedings of the National Academy of Sciences, 115(37): 9216-9221.
Topics: Recent advances in survey research. Multilevel regression and poststratification. Parallel programming with R.
SLIDES (.pdf): Introduction to multilevel models in R.
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.
Muñoz, J., Falcó-Gimeno, A., and Hernández, E. (2019). Unexpected Event during Survey Design: Promise and Pitfalls for Causal Inference. Political Analysis, forthcoming.
Topics: Introduction to automated text analysis. Sources of textual data. String manipulation in R. Regular expressions. Text processing with quanteda.
SLIDES (.pdf): Introduction to automated text analysis. Text preprocessing.
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. (2019). Text as data: an overview. Forthcoming in Curini, L., and Franzese, R. (eds) The SAGE Handbook of Research Methods in Political Science and International Relations, London: Sage, 2019.
Wilkerson, J. and Casas, A. (2017). Large-Scale Computerized Text Analysis in Political Science: Opportunities and Challenges. Annual Review of Political Science, 20, 529:544.
Denny, M. J., & Spirling, A. (2018). Text preprocessing for unsupervised learning: why it matters, when it misleads, and what to do about it. Political Analysis, 26(2): 168-189.
Topics: Dictionary methods applied to sentiment analysis.
SLIDES (.pdf): Dictionary methods.
González-Bailón, S., & Paltoglou, G. (2015). Signals of public opinion in online communication: A comparison of methods and data sources.. The ANNALS of the American Academy of Political and Social Science, 659(1), 95-107.
Barberá et al (2019). Automated Text Classification of News Articles: A Practical Guide. Working paper.
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.
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.
Beauchamp, N. (2017). Predicting and Interpolating State‐Level Polls Using Twitter Textual Data. American Journal of Political Science, 61(2), 490-503.
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).
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.
Barberá et al. (2019). Who Leads? Who Follows? Measuring Issue Attention and Agenda Setting by Legislators and the Mass Public Using Social Media Data. American Political Science Review
Terman, R. (2017). Islamophobia and Media Portrayals of Muslim Women: A Computational Text Analysis of US News Coverage. International Studies Quarterly, 61(2): 489-502.
de Vries, E., Schoonvelde, M. & Schumacher, G. (2018). No Longer Lost in Translation: Evidence that Google Translate Works for Comparative Bag-of-Words Text Applications. Political Analysis, 26(4), 417-430.
Topics: Introduction to word embeddings. Ideological scaling of documents. Event detection.
SLIDES (.pdf): Word embeddings.
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.
Skim: Spirling, A. and Rodriguez, P. (2019). Word Embeddings What works, what doesn’t, and how to tell the difference for applied research. Working paper, New York University
Rodman, E. (2019). A Timely Intervention: Tracking the Changing Meanings of Political Concepts with Word Vectors. Political Analysis.
Rheault, L and Cochrane, C. (2019). Word Embeddings for the Analysis of Ideological Placement in Parliamentary Corpora. Political Analysis.
Topics: Basic concepts in social network analysis. 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.
Iakhnis, E. and Badawy, A. (2019). Networks of Power: Analyzing World Leaders Interactions on Social Media. arXiv prepring, 1907.11283.
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: Querying large-scale online datasets with SQL and Google BigQuery.
SLIDES (.pdf): Introduction to SQL.
Beaulieu, A. (2009). Learning SQL: Master SQL Fundamentals. O’Reilly Media, Inc.
Stephens, R., Plew, R., & Jones, A. D. (2009). Sams teach yourself SQL in one hour a day. Sams Publishing.
Topics: Social contagion processes: homophily vs influence. Collecting social media data.
SLIDES (.pdf): Social contagion processes. Twitter 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.
Shalizi, C.R. and Thomas, A. (2011). Homophily and Contagion Are Generically Confounded in Observational Social Network Studies. Sociological Methods & Research, 40(2): 211-239.
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.
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.
Bakshy, E., Rosenn, I., Marlow, C. and Adamic, L. (2012) The Role of Social Networks in Information Diffusion. ArXiv Preprint: 1201.4145v2.
Topics: Dimensionality reduction. Latent variable models.
SLIDES (.pdf): Principal component analysis. Latent space network models.
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.
González-Bailón et al (2011). The Dynamics of Protest Recruitment through an Online Network. Nature Scientific Reports, 1: 197.
No class: Thanksgiving Holiday.
Student presentations of research projects. Course recap.
Other topics not covered in this year’s version of the course:
Audiovisual data (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.
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.