Course outline and readings

Note: slides will be added as the course progresses.

Week 1: 08/25. Introduction

Topics: Introductions and course overview. What is Computational Social Science? Introduction to version control and GitHub.

LINK TO SLIDES (.pdf)

Background reading

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.

Week 2: 09/01. Ethics.

Topics: Computational social science: research opportunities and challenges. Ethics of social science research in the digital age. Big data, big bias?

LINK TO SLIDES (.pdf)

Background reading

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.

Readings for discussion

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.

Week 3: 09/08. Online experiments.

Topics: Large-scale online experiments. Data collection: automated data collection from the web.

LINK TO SLIDES (.pdf)

Background reading

Guess, A. M. (2021). Experiments Using Social Media Data. Advances in Experimental Political Science, 184.

Readings for discussion

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.

LINK TO SLIDES (.pdf)

Background reading

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. (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.

No readings for discussion this week

Week 5: 09/22. Text (II).

Topics: Describing and comparing textual data.

LINK TO SLIDES (.pdf)

Background reading

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.

Readings for discussion

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.
Hengel, E. (2020). Publishing while female. Chapter 10. Women in Economics, 80.

Week 6: 09/29. Text (III).

Topics: Dictionary methods.

LINK TO SLIDES (.pdf)

Background reading

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.

Readings for discussion

Young, L., & Soroka, S. (2012). Affective news: The automated coding of sentiment in political texts. Political Communication, 29(2), 205-231.
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.

Week 7: 10/06. Text (IV).

Topics: Supervised machine learning applied to text classification. Crowd-sourcing the creation of training datasets.

LINK TO SLIDES (.pdf)

Background reading

Chapters 2, 5, and 6 of James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. New York: Springer.
Grimmer, J., Roberts, M. E., & Stewart, B. M. (2021). Machine Learning for Social Science: An Agnostic Approach. Annual Review of Political Science, 24, 395-419.
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.

Readings for discussion

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.
Peterson, A., & Spirling, A. (2018). Classification accuracy as a substantive quantity of interest: Measuring polarization in westminster systems. Political Analysis, 26(1), 120-128.

Week 8: 10/13. Text (V).

Topics: Unsupervised machine learning (topic models).

LINK TO SLIDES (.pdf)

Background reading

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.
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.

Readings for discussion

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.

Week 9: 10/20. Text (VI).

Topics: Word embeddings.

LINK TO SLIDES (.pdf)

Background reading

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.

Readings for discussion

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.
Béchara, H., Herzog, A., Jankin, S., & John, P. (2021). Transfer learning for topic labeling: Analysis of the UK House of Commons speeches 1935–2014. Research & Politics, 8(2), 20531680211022206.
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).

Week 10: 10/27. Networks (I).

Topics: Basic concepts in social network analysis. Network visualization.

LINK TO SLIDES (.pdf)

Background reading

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.

Readings for discussion

Bisbee, J., & Larson, J. M. (2017). Testing social science network theories with online network data: An evaluation of external validity. American political science review, 111(3), 502-521.
Steinert-Threlkeld, Z. C. (2017). Spontaneous collective action: Peripheral mobilization during the Arab Spring. American Political Science Review, 111(2), 379-403.

Week 11: 11/03. Networks (II).

Topics: Social contagion processes: homophily vs influence. Collecting social media data.

LINK TO SLIDES (.pdf)

Background reading

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.

Readings for discussion

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.