Note: slides may be updated as the course progresses.
Topics: Introductions and course overview. What is Computational Social Science? Introduction to version control and GitHub.
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.
Lazer, D., Hargittai, E., Freelon, D., Gonzalez-Bailon, S., Munger, K., Ognyanova, K., & Radford, J. (2021). Meaningful measures of human society in the twenty-first century. Nature, 1-8.
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.
Topics: Computational social science: research opportunities and challenges. Ethics of social science research in the digital age. Big data, big bias?
Barberá, P. & and Steinert-Threlkeld, Z. (2020). How to Use Social Media Data for Political Science Research. Curini, L., and Franzese, R. (eds) The SAGE Handbook of Research Methods in Political Science and International Relations, London: Sage, 404-423.
Lazer, D. M., Pentland, A., Watts, D. J., Aral, S., Athey, S., Contractor, N., … & Wagner, C. (2020). Computational social science: Obstacles and opportunities. Science, 369(6507), 1060-1062.
Chapter 6 of Salganik, M. (2017). Bit by Bit: Social Research in the Digital Age. Princeton, NJ: Princeton University Press. Open review edition.
Persily, N., & Tucker, J. A. (2020). Conclusion: The Challenges and Opportunities for Social Media Research. In Social Media and Democracy: The State of the Field, Prospects for Reform, 313-324.
Hargittai, E. (2018). Potential Biases in Big Data: Omitted Voices on Social Media. Social Science Computer Review, forthcoming.
Vlasceanu, M., & Amodio, D. M. (2022). Propagation of Societal Gender Inequality by Internet Search Algorithms. Proceedings of the National Academy of Sciences, 119 (29) e2204529119.
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.
Topics: Large-scale online experiments. Data collection: automated data collection from the web.
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.
Siegel, A. A., & Badaan, V. (2020). #No2Sectarianism: Experimental approaches to reducing sectarian hate speech online. American Political Science Review, 114(3), 837-855.
Asimovic, N., Nagler, J., Bonneau, R., & Tucker, J. A. (2021). Testing the effects of Facebook usage in an ethnically polarized setting. Proceedings of the National Academy of Sciences, 118(25).
Topics: Introduction to automated text analysis: key concepts; selecting documents and features. Sources of textual data. String manipulation in R. Regular expressions. Text processing with quanteda.
Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). Text as data: A new framework for machine learning and the social sciences. Princeton University Press. Chapter 2.
Benoit, K. (2020). Text as data: an overview. In Curini, L., and Franzese, R. (eds) The SAGE Handbook of Research Methods in Political Science and International Relations, London: Sage.
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.
Topics: Describing and comparing textual data.
Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). Text as data: A new framework for machine learning and the social sciences. Princeton University Press. Chapter 5, 7, and 11.
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.
Carter, E. B., & Carter, B. L. (2021). Questioning More: RT, Outward-Facing Propaganda, and the Post-West World Order. Security Studies, 30(1), 49-78.
Liu, A. H. (2021). Pronoun Usage as a Measure of Power Personalization: A General Theory with Evidence from the Chinese-Speaking World. British Journal of Political Science, 1-18.
Topics: Dictionary methods.
Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). Text as data: A new framework for machine learning and the social sciences. Princeton University Press. Chapters 15 and 16.
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, P., Boydstun, A. E., Linn, S., McMahon, R., & Nagler, J. (2021). Automated text classification of news articles: A practical guide. Political Analysis, 29(1), 19-42.
Engler, S., Gessler, T., Abou-Chadi, T., & Leemann, L. (2022). Democracy challenged: how parties politicize different democratic principles. Journal of European Public Policy, 1-23.
Rathje, S., Van Bavel, J. J., & van der Linden, S. (2021). Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences, 118(26).
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.
Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). Text as data: A new framework for machine learning and the social sciences. Princeton University Press. Chapters 17, 18, 19, and 20.
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. (2020). The dynamics of political incivility on Twitter. Sage Open, 10(2), 2158244020919447.
Mitts, T., Phillips, G., & Walter, B. (2021). Studying the Impact of ISIS Propaganda Campaigns. Journal of Politics, forthcoming. (Read also Appendix.)
Kim, J. Y. (2021). Integrating human and machine coding to measure political issues in ethnic newspaper articles. Journal of Computational Social Science, 1-28.
Eberhardt, M., Facchini, G., & Rueda, V. (2022). Gender Differences in Reference Letters: Evidence from the Economics Job Market. Working paper.
Topics: Unsupervised machine learning (topic models).
Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). Text as data: A new framework for machine learning and the social sciences. Princeton University Press. Chapters 12 and 13.
Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77-84.
Quinn, K. M., Monroe, B. L., Colaresi, M., Crespin, M. H., & Radev, D. R. (2010). How to analyze political attention with minimal assumptions and costs. American Journal of Political Science, 54(1), 209-228.
Barberá, P., Casas, A., Nagler, J., Egan, P. J., Bonneau, R., Jost, J. T., & Tucker, J. A. (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, 113(4), 883-901.
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.
Motolinia, L. (2021). Electoral Accountability and Particularistic Legislation: Evidence from an Electoral Reform in Mexico. American Political Science Review, 115(1), 97-113.
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: Word embeddings.
Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). Text as data: A new framework for machine learning and the social sciences. Princeton University Press. Chapter 8.
Rodriguez, P., & Spirling, A. (2021). Word Embeddings: What works, what doesn’t, and how to tell the difference for applied research. Journal of Politics, forthcoming. See also FAQ.
Rodman, E. (2020). A timely intervention: Tracking the changing meanings of political concepts with word vectors. Political Analysis, 28(1), 87-111.
Osnabrügge, M., Hobolt, S. B., & Rodon, T. (2021). Playing to the Gallery: Emotive Rhetoric in Parliaments. American Political Science Review, 1-15.
Card, D., Chang, S., Becker, C., Mendelsohn, J., Voigt, R., Boustan, L., … & Jurafsky, D. (2022). Computational analysis of 140 years of US political speeches reveals more positive but increasingly polarized framing of immigration. Proceedings of the National Academy of Sciences, 119(31), e2120510119.
Yang, E., & Roberts, M. E. (2021). Censorship of Online Encyclopedias: Implications for NLP Models. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 537-548).
Topics: Basic concepts in social network analysis. Network visualization.
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). Spontaneous collective action: Peripheral mobilization during the Arab Spring. American Political Science Review, 111(2), 379-403.
Chetty, R., Jackson, M. O., Kuchler, T., Stroebel, J., et al. (2022). Social capital I: measurement and associations with economic mobility. Nature, 1-14.
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.
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
Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146-1151.
Topics: Dimensionality reduction. 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.
Topics: 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.
No class: Thanksgiving Holiday.
Topics: Parallel programming with R. Good coding practices