Lecture covers:

• David Lazer, Alex Pentland, Lada Adamic, Sinan Aral, Albert-lászló Barabási, Devon Brewer, Nicholas Christakis, Noshir Contractor, James Fowler, Myron Gutmann, Tony Jebara, Gary King, Michael Macy, Deb Roy, Marshall Van Alstyne Computational Social Science, Science. 2009.
• Watts, Duncan. Computational Social Science: Exciting progress and future challenges., KDD keynote 2016.

Lecture covers:

• Matthew Salganik. Bit by Bit, Chapter 1: Introduction
• Matthew Salganik. Bit by Bit, Chapter 2: Observing Behavior

Required reading

• Ruths, Derek, and Jürgen Pfeffer. 2014. Social Media for Large Studies of Behavior. Science 346 (6213):1063–4.
• boyd, danah, and Kate Crawford. 2012. Critical Questions for Big Data. Information, Communication & Society 15 (5):662–79.

Additional reading:

• Duncan Watts. Should social science be more solution-oriented?, Nature.
• Gerring, John. Mere Description. British Journal of Political Science 42, no. 4 (2012): 721–46.

Lecture covers:

• Matthew Salganik. Bit by Bit, Chapter 6: Ethics.
• Matthew Salganik. Bit by Bit, Chapter 4: Running experiments.

Required reading:

• Adam Kramer, Jamie Guillory, & Jeffrey Hancock. Emotional Contagion, PNAS. 2014.
• Bond, Robert M., Christopher J. Fariss, Jason J. Jones, Adam DI Kramer, Cameron Marlow, Jaime E. Settle, and James H. Fowler. A 61-million-person experiment in social influence and political mobilization. Nature 489, no. 7415 (2012): 295-298.

Additional reading:

Ethics: Able is not always ethical

• Grimmelmann, J. (2015). The law and ethics of experiments on social media users. 13 Colo. Tech. L.J. 219, 2015.
• Fiesler, Casey, and Nicholas Proferes. “Participant” perceptions of Twitter research ethics.. Social Media+ Society. 2015.
• Kroll, Joshua A., Solon Barocas, Edward W. Felten, Joel R. Reidenberg, David G. Robinson, and Harlan Yu. Accountable algorithms. U. Pa. L. Rev. 165 (2016): 633.
• Matias, J. N. (2016). The Obligation To Experiment. MIT Media Lab.
• Robinson Meyer. Everything We Know About Facebook’s Secret Mood Manipulation Experiment, the Atlantic. June 28, 2014.

Experiments:

• King, Gary, Benjamin Schneer, and Ariel White. How the news media activate public expression and influence national agendas. Science 358, no. 6364 (2017): 776-780.
• Eckles, Dean, René F. Kizilcec, and Eytan Bakshy. Estimating peer effects in networks with peer encouragement designs. Proceedings of the National Academy of Sciences 113, no. 27 (2016): 7316-7322.
• Munger, Kevin. Tweetment effects on the tweeted: Experimentally reducing racist harassment. Political Behavior 39, no. 3 (2017): 629-649.

Lecture covers:

• Matthew Salganik. Bit by Bit, Chapter 3: Asking Questions.
• Jaime Teevan (2013). A Formula for Academic Papers.
• Eamonn Keogh (2009). Tutorial on How to do good research, get it published in SIGKDD and get it cited! (slide #69 on writing the paper). KDD’09.

Required reading:

• Wang, Wei, David Rothschild, Sharad Goel, and Andrew Gelman. Forecasting elections with non-representative polls. International Journal of Forecasting 31, no. 3 (2015): 980-991.
• Joshua Blumenstock, Gabriel Cadamuro, & Robert On. Predicting Poverty and Wealth from Mobile Phone Data, Science 350, no. 6264 (2015): 1073-1076.

Additional reading:

• National Research Council. 2013. Nonresponse in Social Science Surveys: A Research Agenda. Summary + Chapter 1. Edited by Roger Tourangeau and Thomas J. Plewe. Panel on a Research Agenda for the Future of Social Science Data Collection, Committee on National Statistics. Division of Behavioral and Social Sciences and Education. Washington, DC: National Academies Press. (2013).
• Ansolabehere, S. and Schaffner, B.F., 2014. Does survey mode still matter? Findings from a 2010 multi-mode comparison. Political Analysis, pp.285-303. (2014).

Deliver a 5-minutes pitch of your project proposal. See requirements page for more info.

Required reading:

• Pearl, Judea. The seven tools of causal inference, with reflections on machine learning. Communications of the ACM 62, no. 3 (2019): 54-60.
• Judea Pearl, Madelyn Glymour, Nicholas Jewell (2016). Causal Inference in Statistics: A Primer. Chapter 1 Preliminaries: Statistical and Causal Models. Wiley Press.

Additional reading:

• Egami, Naoki, Christian J. Fong, Justin Grimmer, Margaret E. Roberts, and Brandon M. Stewart. 2022. How to make causal inferences using texts., Science Advances 8, no. 42 (2022).
• Dean Eckles & Eytan Bakshy (2020): Bias and high-dimensional adjustment in observational studies of peer effects, Journal of the American Statistical Association. 2020.
• Sharma, Amit, Jake M. Hofman, and Duncan J. Watts. 2015. Estimating the Causal Impact of Recommendation Systems from Observational Data. In Proceedings of the Sixteenth ACM Conference on Economics and Computation, 453–70. EC ’15. New York: ACM.

Required reading:

• Bakshy, Eytan, Solomon Messing, and Lada A. Adamic. Exposure to ideologically diverse news and opinion on Facebook. Science 348, no. 6239 (2015): 1130-1132.
• Chris Bail, et al. Exposure to opposing views on social media can increase political polarization, Proceedings of the National Academy of Sciences.

Additional reading:

• Green, Jon, Stefan McCabe, Sarah Shugars, Hanyu Chwe, Luke Horgan, Shuyang Cao, and David Lazer. Curation bubbles. OSF Preprints. (2023).
• Flaxman, Seth, Sharad Goel, and Justin M. Rao. Filter bubbles, echo chambers, and online news consumption. Public opinion quarterly 80, no. S1 (2016): 298-320.
• Levy, Ro’ee. Social media, news consumption, and polarization: Evidence from a field experiment. American economic review 111, no. 3 (2021): 831-70.
• Wojcieszak, Magdalena, Andreu Casas, Xudong Yu, Jonathan Nagler, and Joshua A. Tucker. Most users do not follow political elites on Twitter; those who do show overwhelming preferences for ideological congruity. Science Advances 8, no. 39 (2022).
• Törnberg, Petter. How digital media drive affective polarization through partisan sorting. Proceedings of the National Academy of Sciences 119, no. 42 (2022): e2207159119.

Required reading:

• Mark Granovetter. The Strength of Weak Ties. American Journal of Sociology 78(6): 1360-1380.
• Nicholas Cristakis & James Fowler. Connected: The Surprising Power of Our Social Networks and How they Shape Our Lives, Chapter One.
• Chetty, Raj, Matthew O. Jackson, Theresa Kuchler, Johannes Stroebel, Nathaniel Hendren, Robert B. Fluegge, Sara Gong et al. Social capital I: measurement and associations with economic mobility. Nature (2022): 1-14.

Additional reading:

• Stanley Milgram. The small world problem, Psychology Today.
• Smriti Bhagat, Moira Burke, Carlos Diuk, Ismail Onur Filiz, and Sergey Edunov. Three and a half degrees of separation. Facebook Research. 2016.
• Burke, Moira, and Robert Kraut. Using Facebook after losing a job: Differential benefits of strong and weak ties. In Proceedings of the 2013 conference on Computer supported cooperative work, pp. 1419-1430. 2013.

Required reading:

• Costello, Thomas H., Gordon Pennycook, and David G. Rand. Durably reducing conspiracy beliefs through dialogues with AI. Science 385, no. 6714 (2024): eadq1814.
• Rathje, Steve, Jay J. Van Bavel, and Sander van der Linden. Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences 118, no. 26 (2021).

Additional reading:

• Pennycook, Gordon, and David G. Rand. Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition 188 (2019): 39-50.
• Kosinski, Michal, David Stillwell, and Thore Graepel. Private traits and attributes are predictable from digital records of human behavior. Proceedings of the national academy of sciences 110, no. 15 (2013): 5802-5805.
• Kozyreva, Anastasia, Philipp Lorenz-Spreen, Ralph Hertwig, Stephan Lewandowsky, and Stefan M. Herzog. Public attitudes towards algorithmic personalization and use of personal data online: Evidence from Germany, Great Britain, and the United States. Humanities and Social Sciences Communications 8, no. 1 (2021): 1-11.

Required reading:

• Hutson, Matthew. 2020. Eye-Catching Advances in Some AI Fields Are Not Real. Science.
• Wu, Xiaolin, and Xi Zhang. Automated inference on criminality using face images. arXiv preprint (2016): 4038-4052.

Additional reading:

Fairness and Reliability

• Yang, Wei, Kuang Lu, Peilin Yang, and Jimmy Lin. Critically Examining the “Neural Hype” Weak Baselines and the Additivity of Effectiveness Gains from Neural Ranking Models. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1129-1132. 2019.
• Justin Grimmer & Brandon Stewart. Text as Data: The Promises and Pitfalls of Automated Content Analysis, Political Analysis.
• Mitchell, Shira, Eric Potash, Solon Barocas, Alexander D’Amour, and Kristian Lum. Prediction-based decisions and fairness: A catalogue of choices, assumptions, and definitions. arXiv preprint arXiv:1811.07867 (2018).
• Kleinberg, Jon, Sendhil Mullainathan, and Manish Raghavan. Inherent trade-offs in the fair determination of risk scores. (2016).
• Sendhil Mullainathan. Biased Algorithms Are Easier to Fix Than Biased People, New York Times. Dec. 6, 2019.

Problems with ML use

• Cathy O’Neil. Chapter 3: Arms Race - Going to College, Weapons of Math Destruction. 2016.
• Alisha Haridasani Gupta. Are Algorithms Sexist?, New York Times. Nov 15, 2019.

Lecture covers:

• Matthew Salganik. Bit by Bit, Chapter 7: The future.
• Baribi-Bartov, Sahar, Briony Swire-Thompson, and Nir Grinberg. Supersharers of fake news on Twitter. Science 384, no. 6699 (2024): 979-982.

Required readings:

• Grinberg, Nir, Kenneth Joseph, Lisa Friedland, Briony Swire-Thompson, and David Lazer. Fake news on Twitter during the 2016 US presidential election. Science 363, no. 6425 (2019): 374-378.
• David M. J. Lazer, Alex Pentland, Duncan J. Watts, Sinan Aral, Susan Athey, Noshir Contractor, Deen Freelon, Sandra Gonzalez-Bailon, Gary King, Helen Margetts, Alondra Nelson, Matthew J. Salganik, Markus Strohmaier, Alessandro Vespignani, Claudia Wagner. 2020. Computational social science: Obstacles and opportunities. Science 369, no. 6507 (2020), pp. 1060-1062.

Additional reading:

Fake News

• Vosoughi, Soroush, Deb Roy, and Sinan Aral. The spread of true and false news online. Science 359, no. 6380 (2018): 1146-1151.
• King, Gary, Jennifer Pan, and Margaret E. Roberts. How the Chinese government fabricates social media posts for strategic distraction, not engaged argument. American political science review 111, no. 3 (2017): 484-501.
• Tsfati, Yariv, Hajo G. Boomgaarden, Jesper Strömbäck, Rens Vliegenthart, Alyt Damstra, and Elina Lindgren. Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Annals of the International Communication Association 44, no. 2 (2020): 157-173.
• Courchesne, Laura, Julia Ilhardt, and Jacob N. Shapiro. Review of social science research on the impact of countermeasures against influence operations. Harvard Kennedy School Misinformation Review (2021).

See requirements page for more info.