Week 1 – Introduction, social data (M 1/12, W 1/14)

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, FAccT.
• Eddy (2005) Antedisciplinary Science, PLOS Comp Bio.

Social science in the age of “big data”:

• Salganik (2017) Bit by Bit, Chapter 2-4.
• Goel et al. (2010) Predicting consumer behavior with Web search, PNAS.
• Choi, Varian (2012) Predicting the Present with Google Trends, The Economic Record.
• Kleinberg et al. (2015) Prediction Policy Problems, AER.

Week 2 – Observational data, causal inference (No Monday class, W 1/21, F 1/23)

Surveys and post-stratification

• Wang et al. (2015) Forecasting elections with non-representative polls, International Journal of Forecasting.
• Gelman et al. (2016) The Mythical Swing Voter, QJPS.
• Rosenzweig et al. (2025) Survey sampling in the Global South using Facebook advertisements, Political Science Research and Methods.
• Westwood (2025) The potential existential threat of large language models to online survey research, PNAS.

The second meeting with be a high-level discussion about the design and evaluation of a recommendation system.

Week 3 – Networks and recommendations (M 1/26, W 1/28)

Diffusion cascades, true and false news:

• Friggeri et al. (2014) Rumor cascades, ICWSM.
• Goel et al. (2016) The Structural Virality of Online Diffusion, Management Science.
• Vosoughi et al. (2018) The spread of true and false news online, Science.
• Allen et al. (2020) Evaluating the fake news problem at the scale of the information ecosystem, Science Advances.
• Hosseinmardi et al. (2021) Examining the consumption of radical content on YouTube, PNAS.
• Juul, Ugander (2022) Comparing information diffusion mechanisms by matching on cascade size, PNAS.

Friend recommendation:

• Ugander et al. (2012) Structural Diversity in Social Contagion, PNAS.
• 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.
• Su et al. (2020) An Experimental Study of Structural Diversity in Social Networks, ICWSM.

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.
• Jahani et al. (2023) Long ties, disruptive life events, and economic prosperity, PNAS.

Week 4 – Product/media recommendations (M 2/2, W 2/4)

• Fleder, Hosanagar (2009) Blockbuster culture’s next rise or fall: The impact of recommender systems on sales diversity, Management Science.
• Kaptein, Eckles (2010) Selecting effective means to any end: futures and ethics of persuasion profiling, Proceedings of the 5th international conference on Persuasive Technology.
• 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.
• Agan, Davenport, Ludwig, Mullainathan (2023) Automating automaticity: How the context of human choice affects the extent of algorithmic bias, NBER working paper.
• Kleinberg, Raghavan, Mullainathan (2022) The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization, EC.
• Chen et al. (2023) Subscriptions and external links help drive resentful users to alternative and extremist YouTube channels, Science Advances.
• Hosseinmardi et al. (2024) Causally estimating the effect of YouTube’s recommender system using counterfactual bots, PNAS.
• Higley et al. (2022) Building and Deploying a Multi-Stage Recommender System with Merlin, RecSys Demonstration track.

Week 5 – Search engines (M 2/9, W 2/11)

• 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 et al. (2006) Topical interests and the mitigation of search engine bias, PNAS.
• Goel et al. (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.
• White, Horvitz (2009) Cyberchondria: Studies of the escalation of medical concerns in Web search, ACM Transactions on Information Systems.
• White, Horvitz (2015) Belief Dynamics and Biases in Web Search, ACM Transactions on Information Systems.
• Williams-Ceci, Macy, Naaman (2023) Trustworthiness Evaluations of Search Results: The Impact of Rank and Misinformation, arXiv.
• Robertson et al. (2023) Users choose to engage with more partisan news than they are exposed to on Google Search, Nature.
• Wan et al. (2025) Searching for Elected Officials: Google’s Prioritization of Political Information, JQD:DM.

Week 6 – Feed algorithms (M 2/16, W 2/18)

Guest lecture: Prof. Jamie Tucker-Foltz, Yale SOM (2/18)

• Bakshy et al. (2012) The role of social networks in information diffusion, WWW.
• Bernstein et al. (2013) Quantifying the invisible audience in social networks, CHI.
• Bakshy, Messing, Adamic (2015) Exposure to ideologically diverse news and opinion on Facebook, Science.
• Flaxman, Goel, Rao (2016) Filter Bubbles, Echo Chambers, and Online News Consumption, Public Opinion Quarterly.
• Bail et al. (2018) Exposure to opposing views on social media can increase political polarization, PNAS.
• Huszar et al. (2021) Algorithmic amplification of politics on Twitter, PNAS.
• Jia et al. (2024) Embedding Democratic Values into Social Media AIs via Societal Objective Functions, CSCW.
• Piccardi et al. (2025) Social Media Algorithms Can Shape Affective Polarization via Exposure to Antidemocratic Attitudes and Partisan Animosity, Science.
• Twitter (2023) Twitter recommendation algorithm, Github.
• X (2025) X For You Feed Algorithm, Github.

Algorithm aversion:

• Dietvorst, Simmons, Massey (2014) Algorithm aversion: People erroneously avoid algorithms after seeing them err, Journal of Experimental Psychology.
• Dietvorst, Simmons, Massey (2016) Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them, Management Science.
• Vaccaro et al. (2017) The Illusion of Control: Placebo Effects of Control Settings, CHI.

Week 7 – Content moderation (M 2/23, W 2/25)

• Crawford, Gillespie (2016) What is a flag for? Social media reporting tools and the vocabulary of complaint, New Media & Society.
• Gillespie (2021) Custodians of the Internet platforms, content moderation, and the hidden decisions that shape social media, Yale University Press.

Birdwatch aka Community Notes:

• Wojcik et al. (2022) Birdwatch: Crowd Wisdom and Bridging Algorithms can Inform Understanding and Reduce the Spread of Misinformation, arXiv.
• Allen, Martel, Rand (2022) Birds of a feather don’t fact-check each other: Partisanship and the evaluation of news in Twitter’s Birdwatch crowdsourced fact-checking program, CHI.
• De et al. (2025) Supernotes: Driving Consensus in Crowd-Sourced Fact-Checking, WWW.
• Slaughter et al. (2025) Community notes reduce engagement with and diffusion of false information online, PNAS.
• Li et al. (2025) Scaling Human Judgment in Community Notes with LLMs, Journal of Online Trust and Safety.
• Vraga (2025) Understanding the strengths and limitations of community-based responses to misinformation, PNAS.
• Renault, Mosleh, Rand (2026) @Grok Is This True? LLM-Powered Fact-Checking on Social Media, pre-print.

Misinformation:

• Pennycook et al. (2021) Shifting attention to accuracy can reduce misinformation online, Nature.
• Martel, Allen, Rand (2023) Crowds Can Effectively Identify Misinformation at Scale, Perspectives on Psychological Science.
• Maldita (2025) Faster, trusted, and more useful: The impact of fact-checkers in X’s Community Notes.
• Allen, Watts, Rand (2024) Quantifying the impact of misinformation and vaccine-skeptical content on Facebook, Science.
• Vranic et al. (2025) Global Claims: A Multilingual Dataset of Fact-Checked Claims with Veracity, Topic, and Salience Annotations, DHOW.

Week 8 – Holistic effects (M 3/2, W 3/4)

Guest lecture: Dr. Stephen Ragain, Reddit (3/2)

• Allcott et al. (2020) The Welfare Effects of Social Media, AER.
• Levy (2021) Social Media, News Consumption, and Polarization: Evidence from a Field Experiment, AER.
• Munger (2021) Facebook is other people.
• Törnberg (2022) How digital media drive affective polarization through partisan sorting, PNAS.
• Allcott et al. (2024) The effects of Facebook and Instagram on the 2020 election: A deactivation experiment, PNAS.
• Allcott et al. (2025) The Effect of Deactivating Facebook and Instagram on Users’ Emotional State, NBER Working Paper.
• Haidt, Bail (2022-2024?) Social Media and Political Dysfunction: A Collaborative Review, Google doc.

(Spring Break)

Week 9 – Network effects and interventions (M 3/23, W 3/25)

• 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, 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.
• Muchnik, Taylor, Aral (2013) Social Influence Bias: A Randomized Experiment, Science.

Diffusion studies:

• Banerjee 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 (2022) 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 et al. (2011) The Anatomy of the Facebook Social Graph, arXiv.
• Kooti, Hodas, Lerman (2014) Network Weirdness: Exploring the Origins of Network Paradoxes, ICWSM.
• Lerman et al. (2016) The Majority Illusion in Social Networks, PLOS One.
• Stewart et al. (2019) Information gerrymandering and undemocratic decisions, Nature.

Week 10 – AI in social environments (M 3/30, W 4/1)

• Lee (2016) Learning from Tay’s introduction, Microsoft blog.
• Kleinberg, Raghavan (2021) Algorithmic monoculture and social welfare, PNAS.
• Park et al. (2022) Social Simulacra: Creating Populated Prototypes for Social Computing Systems, UIST.
• Park et al. (2023) Generative Agents: Interactive Simulacra of Human Behavior, UIST.
• Park et al. (2024) Generative Agent Simulations of 1,000 People, arXiv.

Week 11 – Ad targetting (M 4/6, W 4/8)

Tracking:

• Eckersley (2010) How Unique Is Your Web Browser?, PETS.
• Narayanan (2010) Cookies, Supercookies and Ubercookies: Stealing the Identity of Web Visitors.
• Englehardt, Narayanan (2016) Online Tracking: A 1-million-site Measurement and Analysis, SIGSAC.

Ad personalization:

• Korolova (2011) Privacy Violations Using Microtargeted Ads: A Case Study, Journal of Privacy and Confidentiality.
• Venkatadri et al. (2018) Privacy Risks with Facebook’s PII-Based Targeting: Auditing a Data Broker’s Advertising Interface, IEEE Security and Privacy.
• Kim, Barasz, John (2018) Why Am I Seeing This Ad? The Effect of Ad Transparency on Ad Effectiveness, Journal of Consumer Research.
• Ali et al. (2019) Discrimination through optimization: How Facebook’s ad delivery can lead to skewed outcomes, CSCW.

Week 12 – Meta 2020 Election studies (M 4/13, W 4/15)

• Wagner (2023) Independence by permission, Science.
• González-Bailón et al. (2023) Asymmetric ideological segregation in exposure to political news on Facebook, Science.
• Guess et al. (2023) Reshares on social media amplify political news but do not detectably affect beliefs or opinions, Science.
• Guess et al. (2023) How do social media feed algorithms affect attitudes and behavior in an election campaign?, Science.
• Nyhan et al. (2023) Like-minded sources on Facebook are prevalent but not polarizing, Nature.

Also above:

• González-Bailón et al. (2024) The Diffusion and Reach of (Mis)Information on Facebook During the U.S. 2020 Election, Sociological Science.
• Allcott et al. (2024) The effects of Facebook and Instagram on the 2020 election: A deactivation experiment, PNAS.

Week 13 – Project Presentations (M 4/20, W 4/22)

Exam week – Project Due (No meetings)