Syllabus

Course Description

Due to the growth in electronic sources such as cell phones, Facebook, Twitter, and other online platforms, researches now have enormous amounts of data about every aspect of our lives – from what we buy, to where we go, to who we know, to what we believe. This has led to a revolution in social science, as we are able to measure human behavior with precision largely thought impossible just a decade ago. Computational Social Science is an exciting and emerging field that sits at the intersection of computer science, statistics, and social science. This course provides a hands-on, non-technical introduction to the methods and ideas of Computational Social Science. We will discuss how new online data sources and the methods that are being used to analyze them can shed new light on old social science questions, and also ask brand new questions. We will also explore some of the ethical and privacy challenges of living in a world where big data and algorithmic decision-making have become more commonplace. Each week, students will have the opportunity to try their hand at analyzing big data from sources ranging from online dating profiles to New York City taxicabs to #metoo Tweets and other sources. Note that this course is a 4-credit course that includes a weekly, 2-hour lab component in addition to lecture and discussion.

Note on Readings

This online syllabus has links to the readings that are online. Where possible, we link to open access copies instead of paywalled ones. Students in the course also have access to a folder on Canvas with PDFs of all readings.

Assignments and Grading

• Exams
  • Midterm
  • Final
• Labs
  • Students will complete a lab each week. We will work on labs together in section, but students are expected to complete them at home if they do not finish in section.
  • Read the directions in each lab carefully as you run through the code.
  • Answer the short answer questions and the reflection questions in the lab. Reflection questions at the end of each lab draw on ideas in the readings and lectures.
  • Save the completed notebook. Export the complete notebook, with your answers in it, as a PDF file. Upload that file to Canvas for grading.
• Participation
  • Participation in lecture and lab sections counts toward students' final grade.

Course Schedule

Week 1: Introduction to CSS

• Topics:
  • Technical setup for the course
  • Overview of course
  • Introduction to python
• Lab: Intro and Installation: Python, Jupyter Notebooks
  • Repository: https://github.com/UM-CSS/CSSLabs-NLP
  • Notebook: 0_Intro_to_python_text.ipynb
• Readings
  • Required
    • 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 (New York, NY), 323(5915), 721.
    • Ng, Fiona. 2016. “Tinder Has an In-House Sociologist and Her Job Is to Figure Out What You Want.” Los Angeles Magazine, May 25.

Week 2: Text as Data: CSS and social research, NLP intro

• Topics:
  • Place of text analysis in social research
  • Thinking about text as data
  • Gender and social categories in dating
• Lab: Word Frequencies
  • Repository: https://github.com/UM-CSS/CSSLabs-NLP
  • Notebook: 2_Word_frequencies.ipynb
• Readings
  • Required
    • Coupland, J. (2000). Past the ‘perfect kind of age’? Styling selves and relationships in over‐50s dating advertisements. Journal of Communication, 50(3), 9-30.
    • Bail, C. (2014). The cultural environment: measuring culture with big data. Theory and Society, 43(3-4), 465–482.
  • Required for graduate students
    • Evans, James A. and Pedro Aceves (2016). "Machine Translation: Mining Text for Social Theory." Annual Review of Sociology.
  • Optional
    • Grimmer and Stewart (2013). Text as Data: The Promises and Pitfalls of Automated Content Analysis. Political Analysis.
    • Bo et al. Thumbs up: Sentiment Classification using Machine Learning Techniques.
    • Carley, Kathleen. 1994. “Extracting Culture Through Textual Analysis.” Poetics 22:291-312.
    • Miner, Horace. 1956. “Body Ritual Among the Nacirema” American Anthropologist 58(3): 503-507.
    • Lynn, M., & Bolig, R. (1985). Personal advertisements: Sources of data about relationships. Journal of Social and Personal Relationships, 2(3), 377-383.

Week 3: Text as Data: Sociology of (Online) Dating, NLP Methods

• Topics:
  • Sociology of (online) dating
  • Topic modeling and other advanced text analysis methods
• Lab: Topic Modeling
  • Repository: https://github.com/UM-CSS/CSSLabs-NLP
  • Notebook: 3_Topic_modeling.ipynb
• Readings
  • Required
    • Pepin, Joanna. 2015. “Online Dating Choices, Constrained.” Contexts 14(4):7. (one page)
    • . 2009. "Exactly what to say in a first message." OKCupid Data Blog, September 13.
    • . 2010. "The REAL 'stuff white people like'." OKCupid Data Blog, September 7.
    • DiMaggio, Paul, Manish Nag, and David Blei. 2013. “Exploiting Affinities between Topic Modeling and the Sociological Perspective on Culture: Application to Newspaper Coverage of U.S. Government Arts Funding.” Poetics 41(6):570–606.
  • Required for graduate students
    • Smith, David A., Ryan Cordell, and Elizabeth Maddock Dillon. 2013. “Infectious Texts: Modeling Text Reuse in Nineteenth-Century Newspapers.” Pp. 86–94 in 2013 IEEE International Conference on Big Data. Silicon Valley, CA, USA: IEEE.
    • Alix Rule, Jean-Philippe Cointet, and Peter S. Bearman. 2015. "Lexical shifts, substantive changes, and continuity in State of the Union discourse, 1790–2014." Proceedings of the National Academy of Sciences Vol 112 (35) 10837-10844
  • Optional
    • Mason, Corinne Lysandra. 2016. “Tinder and Humanitarian Hook-Ups: The Erotics of Social Media Racism.” Feminist Media Studies 16(5):822–37.
    • Sumter, Sindy R., Laura Vandenbosch, and Loes Ligtenberg. 2017. “Love Me Tinder: Untangling Emerging Adults’ Motivations for Using the Dating Application Tinder.” Telematics and Informatics 34(1):67–78.

Week 4: Online Experiments and Music Lab

• Topics:
  • Understand culture as a dynamic process rather than fixed.
  • Understand culture as a partially stochastic (random) process.
  • Understand the purpose of experiments and difference from other empirical methods.
• Lab: Experiments
  • Repository: https://github.com/UM-CSS/CSSLabs-Experiments
  • Notebooks: 0_Online_Experiments.ipynb and 1_Power_Calculation.ipynb
• Readings
  • Required
    • Ko, Allen, Mou, Merry, and Matias, J. Nathan. 2016. “The Obligation To Experiment.” MIT Media Lab, Medium.
    • Bertrand, Marianne and Sendhil Mullainathan. 2004. “Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination.” American Economic Review 94(4):991–1013.
    • Salganik, M. J., Dodds, P. S., Watts, D. J. (2006). "Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market." Science, 311(5762), 854–856.
  • Required for graduate students
    • Centola, D., Baronchelli, A. (2015). "The spontaneous emergence of conventions: An experimental study of cultural evolution." Proceedings of the National Academy of Sciences, 112(7), 1989–1994.
    • E. Bakshy, S. Messing, L. A. Adamic (2015) "Exposure to ideologically diverse news and opinion on Facebook." Science
  • Optional
    • Salganik, M.J., and Watts, D.J. (2008). "Leading the herd astray: Experimental study of self-fulfilling prophecies in an artificial cultural market." Social Psychology Quarterly, 71:338-355.
    • Salganik, M.J. and Watts, D.J. (2009) "Web-based Experiments for the Study of Collective Social Dynamics in Cultural Markets." Topics in Cognitive Science, 1(3):439{468, 2009.
    • Bail, Christopher A., Lisa Argyle, Taylor Brown, John Bumpus, Haohan Chen, M. B. Fallin Hunzaker, Jaemin Lee, Marcus Mann, Friedolin Merhout, and Alexander Volfovsky. 2018. “Exposure to Opposing Views Can Increase Political Polarization: Evidence from a Large-Scale Field Experiment on Social Media.” Proceedings of the National Academy of Sciences.

Week 5: Communities and Norms

• Topics:
  • What are communities? Understanding norms, influence, collective feeling
  • Online vs offline communities, effects of platform and envrionment
  • Measurement of social norms and interaction style online
• Lab: Community Dynamics
  • Repository: https://github.com/UM-CSS/CSSLabs-Community-Dynamics
  • Notebooks: 0_Intro_Reddit_Data.ipynb
• Readings
  • Required
    • Voigt, Rob, Nicholas P. Camp, Vinodkumar Prabhakaran, William L. Hamilton, Rebecca C. Hetey, Camilla M. Griffiths, David Jurgens, Dan Jurafsky, and Jennifer L. Eberhardt. 2017. “Language from Police Body Camera Footage Shows Racial Disparities in Officer Respect.” Proceedings of the National Academy of Sciences 114(25):6521–26.
    • Danescu-Niculescu-Mizil, Cristian, Robert West, Dan Jurafsky, Jure Leskovec, and Christopher Potts. 2013. “No Country for Old Members: User Lifecycle and Linguistic Change in Online Communities.” Pp. 307–18 in Proceedings of the 22nd international conference on World Wide Web. ACM.
  • Required for graduate students
    • Collins, Randall. 1981. “On the Microfoundations of Macrosociology.” American Journal of Sociology 86(5):984-1014.
    • Hochschild, Arlie Russel. 1979. “Emotion Work, Feeling Rules and Social Structure.” The American Journal of Sociology 85(3):551-575.
  • Optional
    • Postmes, T. 2000. “The Formation of Group Norms in Computer-Mediated Communication.” Human Communication Research 26(3):341–71.
    • Palmer, Alexis, Melissa Robinson, and Kristy Philips. 2017. “Illegal Is Not a Noun: Linguistic Form for Detection of Pejorative Nominalizations.” Pp. 91–100 in Proceedings of the First Workshop on Abusive Language Online. Vancouver.
    • Danescu-Niculescu-Mizil, Cristian, et al. 2013. "A computational approach to politeness with application to social factors." Proceedings of ACL.
    • Shaw, A., & Hill, B. M. (2014). "Laboratories of oligarchy? How the iron law extends to peer production." Journal of Communication, 64(2), 215-238.
    • Golder, S. A. and M. W. Macy. 2011. “Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures.” Science 333(6051):1878–81.

Week 6: Communities in Flux

• Topics:
  • Civility and harassment online, group identity, prejudice, and anonymity
  • Reddit's Default Day as a natural experiment / exogenous shock
  • Heterogenous treatment effects: not everyone responds the same way
• Lab: Community Dynamics
  • Repository: https://github.com/UM-CSS/CSSLabs-Community-Dynamics
  • Notebooks: 1_Evolution_of_Community_Norms.ipynb and 2_Other_shocks_to_communities.ipynb
• Readings
  • Required
    • Chandrasekharan, Eshwar, Umashanthi Pavalanathan, Anirudh Srinivasan, Adam Glynn, Jacob Eisenstein, and Eric Gilbert. 2017. “You Can’t Stay Here: The Efficacy of Reddit’s 2015 Ban Examined Through Hate Speech.” Proceedings of the ACM on Human-Computer Interaction 1(CSCW):1–22.
    • Hern, Alex. 2014. "Reddit women protest at new front-page position." The Guardian, 13 May.
  • Required for graduate students
    • Massanari, Adrienne. 2017. “#Gamergate and The Fappening: How Reddit’s Algorithm, Governance, and Culture Support Toxic Technocultures.” New Media & Society 19(3):329–46.
  • Optional
    • Franco, V., Piirto, R., Hu, H. Y., Lewenstein, B. V., Underwood, R., & Vidal, N. K. (1995). "Anatomy of a flame: conflict and community building on the Internet." IEEE Technology and Society Magazine, 14(2), 12-21.
    • Gonzalez-Bailon, S., Kaltenbrunner, A., & Banchs, R. E. (2010). "The structure of political discussion networks: a model for the analysis of online deliberation." Journal of Information Technology, 25(2), 230-243.
    • Müller, Karsten and Carlo Schwarz. 2018. "Fanning the Flames of Hate: Social Media and Hate Crime." SSRN Scholarly Paper. ID 3082972. Rochester, NY: Social Science Research Network.
    • Mantilla, Karla. 2013. “Gendertrolling: Misogyny Adapts to New Media.” Feminist Studies 39(2):563–70.
    • Panek, Elliot, Connor Hollenbach, Jinjie Yang, and Tyler Rhodes. 2017. “Growth and Inequality of Participation in Online Communities: A Longitudinal Analysis.” Pp. 51:1–51:5 in Proceedings of the 8th International Conference on Social Media & Society, #SMSociety17. New York, NY, USA: ACM.

Week 7: From Data to Conclusions: Validity and Generalizability

• Topics:
  • Understand sampling, populations, and sample frames.
  • Understand sources of bias in sampling.
  • Understand how valid methods can be used to make bad inferences.
• Lab: Student data
  • Repository: https://github.com/UM-CSS/Drawing-Conclusions
  • Notebook: 0_Drawing_Conclusions.ipynb

• Readings

  • Required

    • Crawford, Kate. 2013. “The Hidden Biases in Big Data.” Harvard Business Review, April 1.
    • Stephens, Alexis. 2014. “Big Data Has Potential to Both Hurt and Help Disadvantaged Communities.” Next City. Retrieved September 29, 2018.
    • Bower, Bruce. 2018. “‘Replication crisis’ spurs reforms in how science studies are done.”
    • Lazer, D. M., Kennedy, R., King, G., & Vespignani, A. (2014). "The parable of Google Flu: Traps in big data analysis." Science 343(6176):1203-1205
    • Kasy, Maximilian. “No Data in the Void: Values and Distributional Conflicts in Empirical Policy Research and Artificial Intelligence.” Research Brief, August 2019.

  • Required for graduate students

    • Goel, S. and Salganik, M.J. (2010) "Assessing respondent-driven sampling." PNAS.
    • Tyler J. VanderWeele and Whitney R. Robinson. 2014. "On causal interpretation of race in regressions adjusting for confounding and mediating variables."
  • Optional
    • Brayne, Sarah. 2017. “Big Data Surveillance: The Case of Policing.” American Sociological Review 82(5):977–1008.
    • . 2015. “Beware Spurious Correlations.” Harvard Business Review.
    • Lisa Gitelman. 2013. Raw Data is an Oxymoron

Week 8: Midterm

• Topics:
  • Review and exam
  • See results of class experiment in lab sections
• Lab: Experiments
  • Repository: https://github.com/UM-CSS/CSSLabs-Experiments
  • Notebooks: 0_Online_Experiments.ipynb and 1_Power_Calculation.ipynb

Week 9: Algorithms and Society

• Topics:
  • Privacy and ethical concerns
  • Real world effects and unintended concequences of algorithmic systems
• Lab: Algorithms and Society
  • Repository: https://github.com/UM-CSS/CSSLabs-Algorithms-Society
  • Notebook: 0_potholes.ipynb
• Readings
  • Required
    • Tufekci, Zeynep. 2018. “Facebook’s Surveillance Machine .” New York Times.
    • Eubanks, Virginia. 2018. “A Child Abuse Prediction Model Fails Poor Families.” Excerpt from Automating Inequality. Wired.
    • Sweeney, L. (2000). "Simple Demographics Often Identify People Uniquely." Carnegie Mellon University Data Privacy Working Paper 3. Pittsburgh 2000.
    • Mattson, Greggor. 2017. “Dating Profiles Are Like Gay Bars: Peer Review, Ethics and LGBTQ Big Data.” Retrieved July 7, 2018.
  • Required for graduate students
    • Gelman, Andrew, Greggor Mattson, and Daniel Simpson. 2018. “Gaydar and the Fallacy of Decontextualized Measurement.” Sociological Science 5:270–80.
    • Kleinberg, Jon, Jens Ludwig, Sendhil Mullainathan, and Ashesh Rambachan. 2018. “Algorithmic Fairness.” AEA Papers and Proceedings 108:22–27.
    • Speer, Rob. 2017. "How to make a racist AI without really trying."
  • Optional
    • Kleinberg, Jon and Sendhil Mullainathan. 2018. “Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability.” ArXiv:1809.04578.
    • Buolamwini, J., & Gebru, T. (2018, January). "Gender shades: Intersectional accuracy disparities in commercial gender classification." In Conference on Fairness, Accountability and Transparency (pp. 77-91).
    • Zimmer, Michael. 2016. "OKCupid Study Reveals the Perils of Big-Data Science." Wired May 14.
    • Lohr, Steve. 2018. “Facial Recognition Is Accurate, If You’re a White Guy.” New York Times, February 9.
    • Angwin, Julia, Jeff Larsen, Surya Mattu, and Lauren Kirchner. 2016. "Machine Bias." ProPublica.
    • Wang, Y., & Kosinski, M. (2018). "Deep neural networks are more accurate than humans at detecting sexual orientation from facial images." Journal of personality and social psychology, 114(2), 246.

Week 10: Networks

• Topics:
  • Understand networks, nodes, and edges.
  • Identify social processes that can be represented as networks.
  • Understand network summary statistics such as centrality.
• Lab: Networks
  • Repository: https://github.com/UM-CSS/CSSLabs-Networks
  • Notebooks: 0_Networks.ipynb and 1_Network_Structure.ipynb
• Readings
  • Required
    • Watts, Duncan. 1999. Small Worlds. Chapter 1. Princeton University Press: 3-8.
    • Healy, K. (2013). "Using Metadata to Find Paul Revere." Blog post.
    • Austin, D. (2006). "How Google Finds Your Needle in the Web's Haystack." American Mathematical Society Feature Column.
  • Required for graduate students
    • Breiger, R. L. (1974). "The duality of persons and groups." Social forces, 53(2), 181-190.

Week 11: Using Networks for Social Science

• Topics:
  • Understand social network structure as social capital.
  • Understand the small world problem and its resolution.
  • Understand how different social processes generate different network structures.
• Lab: Networks
  • Repository: https://github.com/UM-CSS/CSSLabs-Networks
  • Notebook: 2_Social_Rank_and_Hierarchy.ipynb
• Readings
  • Required
    • Granovetter, Mark S. 1973. “The Strength of Weak Ties.” American Journal of Sociology 78(6): 1360-1380.
    • S. Milgram (1967) “The small world problem,” Psychology Today.
    • Watts, D. (2016). "How small is the world, really?" Blog post.
    • Eckert, P. (1989). "The corporate structure of the school." In Jocks and Burnouts. Teacher's College Press.
  • Required for graduate students
    • C. Shalizi, A. Thomas (2011) "Homophily and contagion are generically confounded in observational social network studies." Sociological methods & research.
    • Rapoport, A., & Horvath, W. J. (1961). "A study of a large sociogram." Systems Research and Behavioral Science, 6(4).
  • Optional
    • Merten, D. E. (1997). "The meaning of meanness: Popularity, competition, and conflict among junior high school girls." Sociology of Education.
    • L. Michell, & A. Amos, (1997). "Girls, pecking order and smoking." Social Science & Medicine 44(12).
    • Coleman, James S. 1988. “Social Capital in the Creation of Human Capital.” The American Journal of Sociology 94, Supplement: Organizations and Institutions: Sociological and Economic Approaches to the Analysis of Social Structure: S95- S120.

Week 12: Social Dynamics: Feedback in Social Environments

• Topics:
  • Tipping and cascades
  • Simulating social processes and Agent Based Modeling
• Lab:
  • Repository: https://github.com/UM-CSS/CSSLabs-Contagion
  • Notebook: 0_ABM.ipynb
• Readings
  • Required
    • Hart, Vi, and Case, Nicky. 2014. “The Parable of the Polygons.”
    • Granovetter, Mark. 1978. “Threshold Models of Collective Behavior.” American Journal of Sociology 83(6):1420–43.
    • Bruch, Elizabeth and Jon Atwell. 2015. “Agent-Based Models in Empirical Social Research.” Sociological Methods & Research 44(2):186–221.
  • Required for graduate students
    • Axelrod, R. (1986). "An evolutionary approach to norms." American political science review, 80(4), 1095-1111.
    • Robert, Lionel and Daniel M. Romero. 2015. “Crowd Size, Diversity and Performance.” Pp. 1379–1382 in Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI ’15. New York, NY, USA: ACM.
    • Schelling, Thomas C. 1969. “Models of Segregation.” American Economic Review 59(2):488–93.
  • Optional
    • Axelrod, R. (1987). The evolution of strategies in the iterated prisoner’s dilemma. The dynamics of norms, 1-16.
    • Epstein, J. M. 2002. “Modeling Civil Violence: An Agent-Based Computational Approach.” Proceedings of the National Academy of Sciences 99:7243–50.

Week 13: Social Dynamics: Behavior in Social Networks

• Topics:
  • Social influence and contagion
  • Collective action and online social movements
• Lab:
  • Repository: https://github.com/UM-CSS/CSSLabs-Contagion
  • Notebook: 1_metoo.ipynb
• Readings
  • Required
    • . 2015. “The Social Network Illusion that Tricks Your Mind.” MIT Technology Review.
    • Garber, Megan. 2011. “The contribution conundrum: Why did Wikipedia succeed while other encyclopedias failed?” Nieman Labs.
    • Centola, Damon and Michael Macy. 2007. “Complex Contagions and the Weakness of Long Ties.” American Journal of Sociology 113(3):702–34.
    • State, Bogdan and Lada Adamic. 2015. “The Diffusion of Support in an Online Social Movement: Evidence from the Adoption of Equal-Sign Profile Pictures.” Pp. 1741–1750 in Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, CSCW ’15. New York, NY, USA: ACM.
  • Required for graduate students
    • Axelrod, Robert and D. Scott Bennett “A Landscape Theory of Aggregation.” British Journal of Political Science, Vol. 23:2: 211-233 (1993).
    • Romero, Daniel M., Brendan Meeder, and Jon Kleinberg. 2011. “Differences in the Mechanics of Information Diffusion Across Topics: Idioms, Political Hashtags, and Complex Contagion on Twitter.” Pp. 695–704 in Proceedings of the 20th International Conference on World Wide Web, WWW ’11. New York, NY, USA: ACM.
  • Optional
    • Nickerson, D.W. (2008). "Is voting contagious? Evidence from two field experiments." American Political Science Review, 102(1):49-57.
    • , Brian and Casey Fiesler. 2017. “The Evolution and Consequences of Peer Producing Wikipedia’s Rules.”
    • Taylor, S. J., & Eckles, D. (2018). "Randomized experiments to detect and estimate social influence in networks." In Complex Spreading Phenomena in Social Systems (pp. 289-322). Springer, Cham.
    • , S., Anderson, A., Hofman, J., & Watts, D. J. (2015). "The structural virality of online diffusion." Management Science, 62(1), 180-196.
    • , A., & Woodard, R. (2013). "Estimating the historical and future probabilities of large terrorist events." The Annals of Applied Statistics, 7(4), 1838-1865.

Week 14: The Cutting Edge

• Topics:
  • Civic data.
  • Geographic data and mapping.
  • Quantifying interaction strength across geographic boundaries.
• Lab:
  • Repository: https://github.com/UM-CSS/CSSLabs-Extra
  • Notebook: 0_Taxi.ipynb
• Readings
  • Required
    • Jean, Neal, Marshall Burke, Michael Xie, W. Matthew Davis, David B. Lobell, and Stefano Ermon. 2016. “Combining Satellite Imagery and Machine Learning to Predict Poverty.” Science 353(6301):790–94.
    • Schneider, Todd. 2015. “Analyzing 1.1 Billion NYC Taxi and Uber Trips, with a Vengeance.” Toddwschneider.Com. Nov 17th. Retrieved September 29, 2018.
  • Required for graduate students
    • Tuite, Kathleen, Noah Snavely, Dun-yu Hsiao, Nadine Tabing, and Zoran Popovic. 2011. “PhotoCity: Training Experts at Large-Scale Image Acquisition Through a Competitive Game.” Pp. 1383–1392 in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’11. New York, NY, USA: ACM.
  • Optional
    • Casas and Webb Williams. "Computer Vision for Political Science Research: A Study of Online Protest Images." Working paper.

Week 15: Review