Week 1 – Introduction (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.

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

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

Social science in the age of “big data”:

• Salganik (2017) Bit by Bit, Chapter 2-4.
• 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.

Surveys and 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.
• Westwood (2025) “The potential existential threat of large language models to online survey research,” PNAS.

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, Backstrom, Marlow, Kleinberg. (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, Kamath, Sharma, Ugander, Goel (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, Fraiberger, Bailey, Eckles (2023) “Long ties, disruptive life events, and economic prosperity,” PNAS.

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

• 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, 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.

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, 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.
• White & Horvitz (2013) “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, Green, Ruck, Ognyanova, Wilson, Lazer (2023) “Users choose to engage with more partisan news than they are exposed to on Google Search,” Nature.
• Wan, Guo, Ozturan, Robertson, Lazer (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, 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.
• Huszar et al. (2021) “Algorithmic amplification of politics on Twitter”, PNAS.
• Jia et al. (2023) “Embedding Democratic Values into Social Media AIs via Societal Objective Functions,” arXiv.
• Piccardi et al. (2024) “Social Media Algorithms Can Shape Affective Polarization via Exposure to Antidemocratic Attitudes and Partisan Animosity,” arXiv.

Algorithm aversion:

• B. Dietvorst, J. Simmons, C. Massey (2014) “Algorithm aversion: People erroneously avoid algorithms after seeing them err,” Journal of Experimental Psychology.
• Berkeley Dietvorst, Joseph Simmons, Cade 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, Bakker, Baxter, Saveski (2025) “Supernotes: Driving Consensus in Crowd-Sourced Fact-Checking,” WWW.
• Slaughter, Peytavin, Ugander, Saveski (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,” arXiv.
• Vraga (2025) “Understanding the strengths and limitations of community-based responses to misinformation,” PNAS.

Misinformation:

• Pennycook, Epstein, Mosleh, Arechar, Eckles, Rand (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)

• 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. See also Haidt’s other collaborator reviews.

(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:

• 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”, 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 – Ad targetting (M 3/30, W 4/1)

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, Andreou, Liu, Mislove, Gummadi, Loiseau, Goga (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, Sapiezynski, Bogen, Korolova, Mislove, Riek (2019) “Ad Personalization: Discrimination through optimization: How Facebook’s ad delivery can lead to skewed outcomes,” CSCW.

Week 11 – AI in social environments (M 4/6, W 4/8)

• 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 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)