Week 1

Tues 9/27 – Introduction

• Hofman et al. (2021) “Integrating explanation and prediction in computational social science,” Nature.
• Wagner et al. (2021) “Measuring algorithmically infused societies”, Nature.
• Abebe et al. (2020) “Roles for Computing in Social Change,” FAT.
• Eddy (2005) “Antedisciplinary Science,” PLOS Comp Bio.

Thurs 9/29 – Observational data and counting at scale

• PS1: Released/introduced
• Advanced applied counting, social science with big data.
• Salganik (2017) Bit by Bit, Chapter 2.

Week 2

Tues 10/4 – Causal Inference

• Prediction vs. causal inference in the social sciences.
• Salganik (2017) Bit by Bit, Chapter 4.
• Goel, Hofman, Lahaie, Pennock, Watts (2010) “Predicting consumer behavior with Web search,” PNAS.
• Choi, Varian (2012) “Predicting the Present with Google Trends,” The Economic Record.
• Kleinberg, Ludwig Mullainathan, Obermeyer (2015) “Prediction Policy Problems,” AER.

Thurs 10/6 – Social Algorithms 1: Web search

• Chakrabarti, Frieze, Vera (2005) “The influence of search engines on preferential attachment,” SODA.
• Salganik, Dodds, Watts (2006) “Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market,” Science.
• Fortunato, Flammini, Menczer, and Vespignani (2006) “Topical interests and the mitigation of search engine bias,” PNAS.
• Goel, Broder, Gabrilovich, Pang (2010) “Anatomy of the long tail: ordinary people with extraordinary tastes,” WSDM.
• Brynjolfsson, Hu, Simester (2011) “Goodbye pareto principle, hello long tail: The effect of search costs on the concentration of product sales,” Management Science.

Week 3

Tues 10/11 – Social Algorithms II: Recommender systems

• Fleder and Hosanagar (2009) “Blockbuster culture’s next rise or fall: The impact of recommender systems on sales diversity,” Management Science.
• Dandekar, Goel, Lee (2013) “Biased assimilation, homophily, and the dynamics of polarization,” PNAS.
• Abdollahpouri, Burke, Mobasher (2017) “Controlling popularity bias in learning-to-rank recommendation,” RecSys.
• Chaney, Stewart, Engelhardt (2018) “How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility,” RecSys.
• Anderson et al. (2020) “Algorithmic Effects on the Diversity of Consumption on Spotify”, WWW.
• Kleinberg, Ragavan (2021) “Algorithmic monoculture and social welfare,” PNAS.
• Hardt, Jagadeesan, Mendler-Dünner (2022) “Performative Power”, NeurIPS. // Recommender systems and “steering”.
• Kleinberg, Raghavan, Mullainathan (2022) “The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization,” EC. // Recommendation systems and revealed preferences.

Social Recommender systems:

• Zignani et al. (2014) “Link and Triadic Closure Delay: Temporal Metrics for Social Network Dynamics”, ICWSM.
• Su, Sharma, Goel (2016), “The Effect of Recommendations on Network Structure”, WWW.

Thurs 10/13 – Social Algorithms III: Feed algorithms

• Bakshy, Rosenn, Marlow, Adamic (2010) “The role of social networks in information diffusion,” WWW.
• Bernstein, Bakshy, Burke, Karrer (2013) “Quantifying the invisible audience in social networks,” CHI.
• Bakshy, Messing, Adamic (2015) “Exposure to ideologically diverse news and opinion on Facebook”
• Flaxman, Goel, Rao (2016) “Filter Bubbles, Echo Chambers, and Online News Consumption”, Public Opinion Quarterly.
• Bail, Argyle, Brown, Volfovsky (2018) “Exposure to opposing views on social media can increase political polarization,” PNAS.
• Allen et al. (2020) “Evaluating the fake news problem at the scale of the information ecosystem,” Science Advances.
• Huszar et al. (2021) “Algorithmic amplification of politics on Twitter”, PNAS.
• Hosseinmardi et al. (2021) “Examining the consumption of radical content on YouTube,” PNAS.

Week 4

Tues 10/18 – Network Analysis I (Guest Lecture: Dr. Martin Saveski)

• PS1: Due
• PS2 Released/introduced
• Graph theory, social network analysis, and network science.

Thurs 10/20 – Network Analysis II

Weak ties:

• Granovetter (1973) “The Strength of Weak Ties,” AJS.
• Granovetter (1983) “The Strength of Weak Ties: A Network Theory Revisited,” Sociological Theory.
• Gee, Jones, Burke (2017) “Social Networks and Labor Markets: How Strong Ties Relate to Job Finding on Facebook’s Social Network,” J Labor Economics.
• Gee et al. (2017) “The paradox of weak ties in 55 countries,” Journal of Economic Behavior & Organization.
• Rajkumar et al. (2022) “A causal test of the strength of weak ties,” Science.

Week 5

Tues 10/25 – Social Influence, influence maximization

• Dodds, Watts (2007) “Influentials, Networks, and Public Opinion Formation,” J Consumer Research.
• Kempe, Kleinberg, Tardos (2003) “Maximizing the spread of influence through a social network,” Proceedings of KDD.
• Centola, Macy (2007) “Complex contagions and the weakness of long ties,” AJS.
• Centola (2010) “The spread of behavior in an online social network experiment,” Science.
• Centola (2011) “An experimental study of homophily in the adoption of health behavior,” Science.
• Aral, Muchnik, Sundararajan (2009) “Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks,” PNAS.
• Ugander, Backstrom, Marlow, Kleinberg. (2012) “Structural Diversity in Social Contagion,” PNAS.
• Muchnik, Taylor, Aral (2013) “Social Influence Bias: A Randomized Experiment,” Science.

Thurs 10/27 – Social Contagion, Social Diffusion

Diffusion studies:

• Banarjee et al. (2013) “The Diffusion of Microfinance,” Science.
• Chami et al. (2017) “Diffusion of treatment in social networks and mass drug administration,” Nature Comms.
• Beaman et al. (2021) “Can Network Theory-Based Targeting Increase Technology Adoption?,” AER.

Nomination targetting:

• Pastor-Satorras, Vespignani (2002) “Immunization of complex networks,” PRE.
• Christakis, Fowler (2010) “Social network sensors for early detection of contagious outbreaks,” PLOS One.
• Kim et al. (2015) “Social network targeting to maximise population behaviour change: a cluster randomised controlled trial”, The Lancet.
• Chin, Eckles, Ugander (2021) “Evaluating stochastic seeding strategies in networks,” Management Science.

Friendship paradox:

• Feld (1991). “Why your friends have more friends than you do,” American Journal of Sociology.
• Ugander, Karrer, Backstrom, Marlow (2011) “The Anatomy of the Facebook Social Graph,” arXiv.
• Kooti, Hodas, Lerman (2014) “Network Weirdness: Exploring the Origins of Network Paradoxes”, Proceedings of ICWSM.
• Lerman et al. (2016) “The Majority Illusion in Social Networks,” PLOS One.
• Steward et al. (2019) “Information gerrymandering and undemocratic decisions,” Nature.

True and false news:

• Friggeri et al. (2014) “Rumor cascades,” ICWSM.
• Vosoughi et al. (2018) “The spread of true and false news online,” Science.
• Juul, Ugander (2022) “Comparing information diffusion mechanisms by matching on cascade size,” PNAS.

Week 6

Tues 11/1 – Modern surveys, post-stratification

• Wang, Rothschild, Goel Gelman, (2015) “Forecasting elections with non-representative polls,” Routledge Studies in Global Information, Politics and Society.
• Gelman, Goel, Rivers, Rothschild (2016) “The Mythical Swing Voter,” QJPS.
• Rosenzsweig et al. (2022) “Survey sampling in the Global South using Facebook advertisements,” SocArxiv.

Thurs 11/3 Digital demography

• PS2: Due
• PS3: Released/introduced
• Zagheni, Garimella, Weber, State (2014) “Inferring international and internal migration patterns from twitter data”, WWW.
• Zagheni, Weber (2015) “Demographic research with non-representative internet data,” International Journal of Manpower.
• Tufecki (2014) “Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls,” ICWSM.
• Zagheni, Weber, Gummadi (2017) “Leveraging Facebook’s advertising platform to monitor stocks of migrants,” Population and Development Review.

Week 7

Tues 11/8 – No lecture, Election Day!

• Project Proposal: Due

Thurs 11/10 – COVID and mobility (Guest lecture: Serina Chang)

• Chang et al. (2020) “Mobility network models of COVID-19 explain inequities and inform reopening,” Nature.

Week 8

Tues 11/15 – Cell phone and mobility data

• de Montjoye et al. (2013) “Unique in the Crowd: The privacy bounds of human mobility,” Scientific Reports.
• Blumenstock, Cadamuro, On (2015) “Predicting poverty and wealth from mobile phone metadata”, Science.
• Aiken et al. (2022) “Machine learning and phone data can improve targeting of humanitarian aid”, Nature.
• Barbosa et al. (2018) “Human mobility: Models and applications,” Physics Reports.
• Chen, Rohla (2018) “The effect of partisanship and political advertising on close family ties”, Science.
• Alessandretti et al. (2020) “The scales of human mobility,” Nature.
• Athey et al. (2021) “Estimating experienced racial segregation in US cities using large-scale GPS data,” PNAS.
• Coston et al. (2021) “Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy,” FAccT.

Thurs 11/17 – Data Privacy

• PS3: Due
• Digital exhaust
• Data privacy models
• Differential privacy

(Thanksgiving break)

Week 9

Tues 11/29 – Guest Lecture, Jenny Hong on Project Recon

Thurs 12/1 – Guest Lecture, Industry speaker (TBD)

Week 10

Tues 12/6 – Project Presentations

Thurs 12/8 – Project Presentations

Exam week

Tues 12/13 Project reports due, 5pm PT