October 11, 2024

Thesopranosblog

It's Your Education

Human social sensing is an untapped resource for computational social science

  • 1.

    Tomasello, M. Becoming Human: A Theory of Ontogeny (Belknap, 2019).

  • 2.

    Galesic, M. et al. Asking about social circles improves election predictions. Nat. Hum. Behav. 2, 187–193 (2018). Shows that human social sensing outperformed traditional polling questions in forecasting for the 2016 US and 2017 French elections.

    Article 

    Google Scholar
     

  • 3.

    Olsson, H., Bruine de Bruin, W., Galesic, M. & Prelec, D. Election polling is not dead: a Bayesian bootstrap method yields accurate forecasts. Preprint at https://doi.org/10.31219/osf.io/nqcgs (2021). Developed an information integration method that provided accurate forecasts of the 2018 and 2020 US elections by combining own intentions with human social sensor reports.

  • 4.

    Bruine de Bruin, W. et al. Asking about social circles improves election predictions even with many political parties. Preprint at https://doi.org/10.31219/osf.io/8g5ce (2021). Shows that human social sensing outperformed traditional polling questions in forecasting for the 2017 Dutch and 2018 Swedish elections.

  • 5.

    Bruine de Bruin, W., Parker, A. M., Galesic, M. & Vardavas, R. Reports of social circles’ and own vaccination behavior: a national longitudinal survey. Health Psychol. 38, 975–983 (2019). Shows that perceived social circle vaccination coverage helps to predict own future vaccination behaviour.

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 6.

    Christakis, N. A. & Fowler, J. H. Social network sensors for early detection of contagious outbreaks. PLoS One 5, e12948 (2010). Shows that asking people about their friends helps to predict outbreaks of contagious diseases.

    PubMed 
    PubMed Central 
    Article 
    ADS 
    CAS 

    Google Scholar
     

  • 7.

    Graefe, A. Accuracy of vote expectation surveys in forecasting elections. Public Opin. Q. 78, 204–232 (2014). Shows that the people’s expectations about the election winner help to forecast US elections from 1932 to 2012.

    Article 

    Google Scholar
     

  • 8.

    Berg, J. E., Nelson, F. D. & Rietz, T. A. Prediction market accuracy in the long run. Int. J. Forecast. 24, 285–300 (2008). Shows that prediction markets outperformed polls in forecasting US elections from 1988 to 2004.

    Article 

    Google Scholar
     

  • 9.

    Rothschild, D. M. & Wolfers, J. Forecasting elections: Voter intentions versus expectations. SSRN Electron. J. https://doi.org/10.2139/ssrn.1884644 (2011). Shows that accuracy of human social sensing is likely to stem from people’s knowledge about their immediate social environments.

  • 10.

    Garcia-Herranz, M., Moro, E., Cebrian, M., Christakis, N. A. & Fowler, J. H. Using friends as sensors to detect global-scale contagious outbreaks. PLoS One 9, e92413 (2014). Shows that monitoring the friends of randomly selected Twitter users helps to predict the use of novel hashtags a week earlier than monitoring random users.

    PubMed 
    PubMed Central 
    Article 
    ADS 
    CAS 

    Google Scholar
     

  • 11.

    Galesic, M., Olsson, H., Dalege, J., van der Does, T. & Stein, D. L. Integrating social and cognitive aspects of belief dynamics: towards a unifying framework. J. R. Soc. Interface 18, rsif.2020.0857 (2021). Introduces a unifying framework for modelling both social and cognitive aspects of belief dynamics.

    Article 

    Google Scholar
     

  • 12.

    van der Does, T., Stein, D. L., Fedoroff, N. & Galesic, M. Moral and social foundations of beliefs about scientific issues: predicting and understanding belief change. Preprint at https://doi.org/10.31219/osf.io/zs7dq (2021). Develops a statistical-physics-inspired model to show that belief change is more likely when educational interventions decrease belief dissonance and at the same time highlight this dissonance.

  • 13.

    Dalege, J. & van der Does, T. Changing beliefs about scientific issues: the role of moral and social belief networks. Preprint at https://arxiv.org/abs/2102.10751 (2021). Shows that dissonance in one’s reports on moral and social beliefs predicts belief change.

  • 14.

    Happé, F., Cook, J. L. & Bird, G. The structure of social cognition: in(ter)dependence of sociocognitive processes. Annu. Rev. Psychol. 68, 243–267 (2017).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 15.

    Krueger, J. I. & Funder, D. C. Towards a balanced social psychology: causes, consequences, and cures for the problem-seeking approach to social behavior and cognition. Behav. Brain Sci. 27, 313–327, discussion 328–376 (2004).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 16.

    Ross, L., Greene, D. & House, P. The “false consensus effect”: an egocentric bias in social perception and attribution processes. J. Exp. Soc. Psychol. 13, 279–301 (1977).

    Article 

    Google Scholar
     

  • 17.

    Chambers, J. R. & Windschitl, P. D. Biases in social comparative judgments: the role of nonmotivated factors in above-average and comparative-optimism effects. Psychol. Bull. 130, 813–838 (2004).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 18.

    Moreno, J. L. Sociometry, Experimental Method and the Science of Society (Beacon House, 1951).

  • 19.

    Goodman, L. A. Snowball sampling. Ann. Math. Stat. 32, 148–170 (1961).

    MathSciNet 
    MATH 
    Article 

    Google Scholar
     

  • 20.

    Heckathorn, D. D. Respondent-driven sampling: a new approach to the study of hidden populations. Soc. Probl. 44, 174–199 (1997).

    Article 

    Google Scholar
     

  • 21.

    Killworth, P. D., Johnsen, E. C., McCarty, C., Shelley, G. A. & Bernard, H. R. A social network approach to estimating seroprevalence in the United States. Soc. Networks 20, 23–50 (1998).

    Article 

    Google Scholar
     

  • 22.

    Centola, D. How Behavior Spreads: The Science of Complex Contagions (Princeton Univ. Press, 2018).

  • 23.

    Christakis, N. A. & Fowler, J. H. Connected: The Amazing Power of Social Networks and How They Shape Our Lives (Harper Collins, 2010).

  • 24.

    Moldoveanu, M. C. & Baum, J. A. C. Epinets: The Epistemic Structure and Dynamics of Social Networks (Stanford Univ. Press, 2014).

  • 25.

    Keusch, F. & Kreuter, F. in Handbook of Computational Social Science, Volume 1 Theory, Case Studies and Ethics (eds Engel, U., Quan-Haase, A., Xun Lui, S. & Lyberg, L. E.) (Routledge, 2021). Describes different sources of digital trace data and issues related to their use in the computational social sciences, including inferential challenges, measures of reproducibility and replicability, and transparency.

  • 26.

    Kreuter, F., Haas, G.-C., Keusch, F., Bähr, S. & Trappmann, M. Collecting survey and smartphone sensor data with an app: opportunities and challenges around privacy and informed consent. Soc. Sci. Comput. Rev. 38, 533–549 (2020).

    Article 

    Google Scholar
     

  • 27.

    Lazer, D. & Radford, J. Data ex machina: introduction to big data. Annu. Rev. Sociol. 43, 19–39 (2017).

    Article 

    Google Scholar
     

  • 28.

    Varian, H. R. Big data: new tricks for econometrics. J. Econ. Perspect. 28, 3–28 (2014).

    Article 

    Google Scholar
     

  • 29.

    Feld, S. L. & McGail, A. Egonets as systematically biased windows on society. Netw. Sci. 8, 399–417 (2020). Discusses how the friendship paradox biases the information that people receive from their social environments.

    Article 

    Google Scholar
     

  • 30.

    Lee, E. et al. Homophily and minority-group size explain perception biases in social networks. Nat. Hum. Behav. 3, 1078–1087 (2019).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 31.

    Lerman, K., Yan, X. & Wu, X.-Z. The “majority illusion” in social networks. PLoS One 11, e0147617 (2016).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 32.

    Chakraborti, A., Toke, I. M., Patriarca, M. & Abergel, F. Econophysics review: II. Agent-based models. Quant. Finance 11, 1013–1041 (2011).

    MathSciNet 
    Article 

    Google Scholar
     

  • 33.

    Edelmann, A., Wolff, T., Montagne, D. & Bail, C. A. Computational social science and sociology. Annu. Rev. Sociol. 46, 61–81 (2020).

    Article 

    Google Scholar
     

  • 34.

    Epstein, J. M. Agent_Zero: Toward Neurocognitive Foundations for Generative Social Science (Princeton Univ. Press, 2014).

  • 35.

    Miller, J. H. & Page, S. E. Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton Univ. Press, 2007).

  • 36.

    Pentland, A. Social Physics: How Social Networks Can Make Us Smarter (Penguin, 2014). Describes applications of statistical physics and other analogies for modelling complex social systems.

  • 37.

    Proskurnikov, A. V. & Tempo, R. A tutorial on modeling and analysis of dynamic social networks. Part I. Annu. Rev. Contr. 43, 65–79 (2017).

    Article 

    Google Scholar
     

  • 38.

    Jung, J., Bramson, A., Crano, W., Page, S. E. & Miller, J. H. Cultural drift, indirect minority influence, network structure and their impacts on cultural change and diversity. Am. Psychol. (in the press).

  • 39.

    Geanakoplos, J. et al. Getting at systemic risk via an agent-based model of the housing market. Am. Econ. Rev. 102, 53–58 (2012).

    Article 

    Google Scholar
     

  • 40.

    Hammond, R., Ornstein, J. T., Purcell, R., Haslam, M. D., & Kasman, M. Modeling robustness of COVID-19 containment policies. Preprint at https://doi.org/10.31219/osf.io/h5ua7 (2021).

  • 41.

    Nowak, A., Szamrej, J. & Latané, B. From private attitude to public opinion: a dynamic theory of social impact. Psychol. Rev. 97, 362–376 (1990).

    Article 

    Google Scholar
     

  • 42.

    Vallacher, R. R., Read, S. J. & Nowak, A. Computational Social Psychology (Routledge, 2017).

  • 43.

    Enns, P. K., Lagodny, J. & Schuldt, J. P. Understanding the 2016 US presidential polls: the importance of hidden Trump supporters. Stat. Politics Policy 8, 41–63 (2017).

    Article 

    Google Scholar
     

  • 44.

    Krumpal, I. Determinants of social desirability bias in sensitive surveys: a literature review. Qual. Quant. 47, 2025–2047 (2013).

    Article 

    Google Scholar
     

  • 45.

    Wang, D., Szymanski, B. K., Abdelzaher, T., Ji, H. & Kaplan, L. The age of social sensing. Computer 52, 36–45 (2019).

    Article 

    Google Scholar
     

  • 46.

    Tucker, J. et al. Social media, political polarization, and political disinformation: a review of the scientific literature. SSRN https://doi.org/10.2139/ssrn.3144139 (2018).

  • 47.

    Smaldino, P. E., Flamson, T. J. & McElreath, R. The evolution of covert signaling. Sci. Rep. 8, 4905 (2018).

    PubMed 
    PubMed Central 
    Article 
    ADS 
    CAS 

    Google Scholar
     

  • 48.

    Alipourfard, N., Nettasinghe, B., Abeliuk, A., Krishnamurthy, V. & Lerman, K. Friendship paradox biases perceptions in directed networks. Nat. Commun. 11, 707 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar
     

  • 49.

    Sudman, S. & Bradburn, N. M. Asking Questions: A Practical Guide to Questionnaire Design (Jossey-Bass, 1982).

  • 50.

    Chin, A. & Bruine de Bruin, W. Understanding the formation of consumers’ stock market expectations. J. Consum. Aff. 51, 200–210 (2017).

    Article 

    Google Scholar
     

  • 51.

    Bruine de Bruin, W., Parker, A. M. & Fischhoff, B. Can adolescents predict significant life events? J. Adolesc. Health 41, 208–210 (2007).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 52.

    Bruine de Bruin, W., Downs, J. S., Murray, P. & Fischhoff, B. Can female adolescents tell whether they will test positive for Chlamydia infection? Med. Decis. Making 30, 189–193 (2010).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 53.

    Hurd, M. D. & McGarry, K. The predictive validity of subjective probabilities of survival. Econ. J. (Lond.) 112, 966–985 (2002).

    Article 

    Google Scholar
     

  • 54.

    Lewis-Beck, M. S. & Tien, C. Voters as forecasters: a micromodel of election prediction. Int. J. Forecast. 15, 175–184 (1999).

    Article 

    Google Scholar
     

  • 55.

    Murr, A. E. The wisdom of crowds: what do citizens forecast for the 2015 British General Election? Elect. Stud. 41, 283–288 (2016).

    Article 

    Google Scholar
     

  • 56.

    Spann, M. & Skiera, B. Internet-based virtual stock markets for business forecasting. Manage. Sci. 49, 1310–1326 (2003).

    Article 

    Google Scholar
     

  • 57.

    Polgreen, P. M., Nelson, F. D., Neumann, G. R. & Weinstein, R. A. Use of prediction markets to forecast infectious disease activity. Clin. Infect. Dis. 44, 272–279 (2007).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 58.

    Spann, M. & Skiera, B. Sports forecasting: a comparison of the forecast accuracy of prediction markets. J. Forecast. 28, 55–72 (2009).

    MathSciNet 
    Article 

    Google Scholar
     

  • 59.

    Jussim, L. Social Perception and Social Reality: Why Accuracy Dominates Bias and Self-Fulfilling Prophecy (Oxford Univ. Press, 2012).

  • 60.

    Brunswik, E. The Conceptual Framework of Psychology (Univ. Chicago Press, 1952).

  • 61.

    Fiedler, K. Beware of samples! A cognitive-ecological sampling approach to judgment biases. Psychol. Rev. 107, 659–676 (2000).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 62.

    Fiedler, K. & Juslin, P. Information Sampling and Adaptive Cognition (Cambridge Univ. Press, 2006).

  • 63.

    Gigerenzer, G., Hertwig, R. & Pachur, T. Heuristics: The Foundations of Adaptive Behavior (Oxford Univ. Press, 2011).

  • 64.

    Pachur, T., Hertwig, R. & Rieskamp, J. Intuitive judgments of social statistics: how exhaustive does sampling need to be? J. Exp. Soc. Psychol. 49, 1059–1077 (2013).

    Article 

    Google Scholar
     

  • 65.

    Simon, H. A. Models of Man; Social and Rational (Wiley, 1957).

  • 66.

    Hertwig, R. & Hoffrage, U. Simple Heuristics in a Social World (Oxford Univ. Press, 2013).

  • 67.

    Schulze, C., Hertwig, R. & Pachur, T. Who you know is what you know: modeling boundedly rational social sampling. J. Exp. Psychol. Gen. 150, 221–241 (2021).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 68.

    McPherson, M., Smith-Lovin, L. & Cook, J. M. Birds of a feather: homophily in social networks. Annu. Rev. Sociol. 27, 415–444 (2001).

    Article 

    Google Scholar
     

  • 69.

    Galesic, M., Olsson, H. & Rieskamp, J. A sampling model of social judgment. Psychol. Rev. 125, 363–390 (2018). Develops a computational model of social judgment based on people’s perceptions of their immediate social environment.

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 70.

    Frable, D. E. S. Being and feeling unique: statistical deviance and psychological marginality. J. Pers. 61, 85–110 (1993).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 71.

    Kruger, J. Lake Wobegon be gone! The “below-average effect” and the egocentric nature of comparative ability judgments. J. Pers. Soc. Psychol. 77, 221–232 (1999).

    CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar
     

  • 72.

    Kruger, J. & Dunning, D. Unskilled and unaware of it: how difficulties in recognizing one’s own incompetence lead to inflated self-assessments. J. Pers. Soc. Psychol. 77, 1121–1134 (1999).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 73.

    Sudman, S. & Kalton, G. New developments in the sampling of special populations. Annu. Rev. Sociol. 12, 401–429 (1986).

    Article 

    Google Scholar
     

  • 74.

    Feld, S. L. Why your friends have more friends than you do. Am. J. Sociol. 96, 1464–1477 (1991). Introduces the friendship paradox, the phenomenon in which the mean number of friends of friends is always greater than the mean number of friends of individuals.

    Article 

    Google Scholar
     

  • 75.

    Cohen, R., Havlin, S. & Ben-Avraham, D. Efficient immunization strategies for computer networks and populations. Phys. Rev. Lett. 91, 247901 (2003).

    PubMed 
    Article 
    ADS 
    CAS 
    PubMed Central 

    Google Scholar
     

  • 76.

    Kumar, V., Krackhardt, D. & Feld, S. Interventions with inversity in unknown networks can help regulate contagion. Preprint at https://arxiv.org/abs/2105.08758 (2021). Based on the logic underlying the friendship paradox, this paper develops strategies to use reports from random individuals to identify better connected individuals in networks where the overall structures are unknown or evolving.

  • 77.

    Kim, D. A. et al. Social network targeting to maximise population behaviour change: a cluster randomised controlled trial. Lancet 386, 145–153 (2015).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 78.

    Aggarwal, C. C. & Abdelzaher, T. in Managing and Mining Sensor Data 237–297 (Springer, 2013).

  • 79.

    Szell, M., Lambiotte, R. & Thurner, S. Multirelational organization of large-scale social networks in an online world. Proc. Natl Acad. Sci. USA 107, 13636–13641 (2010).

    CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar
     

  • 80.

    Centola, D. & Macy, M. Complex contagions and the weakness of long ties. Am. J. Sociol. 113, 702–734 (2007).

    Article 

    Google Scholar
     

  • 81.

    Guilbeault, D., Baronchelli, A. & Centola, D. Experimental evidence for scale-induced category convergence across populations. Nat. Commun. 12, 327 (2021).

    CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar
     

  • 82.

    Bruch, E. & Mare, R. D. Neighborhood choice and neighborhood change. Am. J. Sociol. 112, 667–709 (2006).

    Article 

    Google Scholar
     

  • 83.

    Bruch, E., Feinberg, F. & Lee, K. Y. Extracting multistage screening rules from online dating activity data. Proc. Natl Acad. Sci. USA 113, 10530–10535 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 84.

    Yang, V. C., Abrams, D. M., Kernell, G. & Motter, A. E. Why are U.S. parties so polarized? A “satisficing” dynamical model. SIAM Rev. 62, 646–657 (2020).

    MathSciNet 
    MATH 
    Article 

    Google Scholar
     

  • 85.

    Dalege, J. et al. Toward a formalized account of attitudes: The Causal Attitude Network (CAN) model. Psychol. Rev. 123, 2–22 (2016). Introduces a measurement model of attitudes relying on network principles.

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 86.

    Jackson, M. O. Social and Economic Networks (Princeton Univ. Press, 2010).

  • 87.

    Jędrzejewski, A. & Sznajd-Weron, K. Statistical physics of opinion formation: is it a SPOOF? C. R. Phys. 20, 244–261 (2019).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • 88.

    Redner, S. Reality-inspired voter models: a mini-review. C. R. Phys. 20, 275–292 (2019).

    CAS 
    Article 
    ADS 

    Google Scholar
     

  • 89.

    Simon, H. A. Invariants of human behavior. Annu. Rev. Psychol. 41, 1–20 (1990).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 90.

    Durkheim, E. The Division of Labour in Society (trans. Simpson, G.) (Free Press, 1893).

  • 91.

    Thomas, W. I. & Swaine Thomas, D. The Child in America: Behaviour Problems and Programs (Knopf, 1928).

  • 92.

    DiMaggio, P. Culture and cognition. Annu. Rev. Sociol. 23, 263–287 (1997).

    Article 

    Google Scholar
     

  • 93.

    Johnson, C., Dowd, T. J. & Ridgeway, C. L. Legitimacy as a social process. Annu. Rev. Sociol. 32, 53–78 (2006).

    Article 

    Google Scholar
     

  • 94.

    Newman, M. E. J. Network structure from rich but noisy data. Nat. Phys. 14, 542–545 (2018).

    CAS 
    Article 

    Google Scholar
     

  • 95.

    Smith, E. R. & Zárate, M. A. Exemplar-based model of social judgment. Psychol. Rev. 99, 3–21 (1992).

    Article 

    Google Scholar
     

  • 96.

    Denrell, J. Why most people disapprove of me: experience sampling in impression formation. Psychol. Rev. 112, 951–978 (2005).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 97.

    Gonzalez, C., Ben-Asher, N., Martin, J. M. & Dutt, V. A cognitive model of dynamic cooperation with varied interdependency information. Cogn. Sci. 39, 457–495 (2015).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 98.

    Cialdini, R. B. & Goldstein, N. J. Social influence: compliance and conformity. Annu. Rev. Psychol. 55, 591–621 (2004).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 99.

    Efferson, C., Lalive, R., Richerson, P. J., McElreath, R. & Lubell, M. Conformists and mavericks: the empirics of frequency-dependent cultural transmission. Evol. Hum. Behav. 29, 56–64 (2008).

    Article 

    Google Scholar
     

  • 100.

    McElreath, R., Wallin, A. & Fasolo, B. in Simple Heuristics in a Social World (eds Hertwig, R. & Hoffrage, U.) 381–408 (Oxford Univ. Press, 2013).

  • 101.

    Tump, A. N., Pleskac, T. J. & Kurvers, R. H. J. M. Wise or mad crowds? The cognitive mechanisms underlying information cascades. Sci. Adv. 6, eabb0266 (2020).

    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar
     

  • 102.

    Moussaïd, M., Kämmer, J. E., Analytis, P. P. & Neth, H. Social influence and the collective dynamics of opinion formation. PLoS One 8, e78433 (2013).

    PubMed 
    PubMed Central 
    Article 
    ADS 
    CAS 

    Google Scholar
     

  • 103.

    Analytis, P. P., Barkoczi, D. & Herzog, S. M. Social learning strategies for matters of taste. Nat. Hum. Behav. 2, 415–424 (2018).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 104.

    Molleman, L. et al. Strategies for integrating disparate social information. Proc. R. Soc. Lond. B 287, 20202413 (2020).


    Google Scholar
     

  • 105.

    Boyd, R. & Richerson, P. J. Culture and the Evolutionary Process (Univ. Chicago Press, 1985).

  • 106.

    Degroot, M. H. Reaching a consensus. J. Am. Stat. Assoc. 69, 118–121 (1974).

    MATH 
    Article 

    Google Scholar
     

  • 107.

    Condorcet, M. Essai sur l’Application de l’Analyse à la Probabilité des Décisions Rendues à la Pluralité des Voix [Essay on the Application of Analysis to the Probability of Majority Decisions] (Imprimerie Royale, 1785).

  • 108.

    Krapivsky, P. L. & Redner, S. Dynamics of majority rule in two-state interacting spin systems. Phys. Rev. Lett. 90, 238701 (2003).

    CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar
     

  • 109.

    Hoppitt, W. & Laland, K. N. Social Learning: An Introduction to Mechanisms, Methods, and Models (Princeton Univ. Press, 2013).

  • 110.

    Galesic, M. & Stein, D. L. Statistical physics models of belief dynamics: theory and empirical tests. Phys. A Stat. Mech. Appl. 519, 275–294 (2019).

    Article 

    Google Scholar
     

  • 111.

    Marr, D. Vision (Freeman, 1982).

  • 112.

    Anderson, J. R. The Adaptive Character of Thought (Lawrence Erlbaum, 1990).

  • 113.

    Chater, N., Tenenbaum, J. B. & Yuille, A. Probabilistic models of cognition: conceptual foundations. Trends Cogn. Sci. 10, 287–291 (2006).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 114.

    Baker, C., Saxe, R., Tenenbaum, J. & Baker, C. L. Bayesian theory of mind: modeling joint belief-desire attribution. In Proc. Annual Meeting of the Cognitive Science Society 2469–2474 (2011).

  • 115.

    Krafft, P. M., Shmueli, E., Griffiths, T. L., Tenenbaum, J. B. & Pentland, A. S. Bayesian collective learning emerges from heuristic social learning. Cognition 212, 104469 (2021).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 116.

    Baltag, A., Christoff, Z., Rendsvig, R. K. & Smets, S. Dynamic epistemic logics of diffusion and prediction in social networks. Stud. Log. 107, 489–531 (2019).

    MathSciNet 
    MATH 
    Article 

    Google Scholar
     

  • 117.

    Schweighofer, S., Schweitzer, F. & Garcia, D. A weighted balance model of opinion hyperpolarization. JASSS 23, 5 (2020). Proposes a model of belief change based on balanced networks accounting for the importance of related issues and other individuals.

    Article 

    Google Scholar
     

  • 118.

    Schweitzer, F., Krivachy, T. & Garcia, D. An agent-based model of opinion polarization driven by emotions. Complexity 2020, 1–11 (2020).

    Article 

    Google Scholar
     

  • 119.

    Castellano, C., Fortunato, S. & Loreto, V. Statistical physics of social dynamics. Rev. Mod. Phys. 81, 591–646 (2009). Reviews many models of social dynamics inspired by analogies from statistical physics.

    Article 
    ADS 

    Google Scholar
     

  • 120.

    Perc, M. et al. Statistical physics of human cooperation. Phys. Rep. 687, 1–51 (2017).

    MathSciNet 
    MATH 
    Article 
    ADS 

    Google Scholar
     

  • 121.

    Dalege, J., Borsboom, D., van Harreveld, F. & van der Maas, H. L. J. The attitudinal entropy (AE) framework as a general theory of individual attitudes. Psychol. Inq. 29, 175–193 (2018). Develops a general theory on individual attitudes using a statistical physics framework.

    Article 

    Google Scholar
     

  • 122.

    Minh Pham, T., Kondor, I., Hanel, R. & Thurner, S. The effect of social balance on social fragmentation. J. R. Soc. Interface 17, 20200752 (2020).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 123.

    Rodriguez, N., Bollen, J. & Ahn, Y.-Y. Collective dynamics of belief evolution under cognitive coherence and social conformity. PLoS One 11, e0165910 (2016). Proposes a belief change model that combines social and cognitive factors as interacting networks driven towards stable triads.

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 124.

    Salganik, M. J. Bit by Bit: Social Research in the Digital Age (Princeton Univ. Press, 2018).

  • 125.

    Pikler, A. G. Utility theories in field physics and mathematical economics (I). Br. J. Philos. Sci. 5, 47–58 (1954).

    Article 

    Google Scholar
     

  • 126.

    Festinger, L. A Theory of Cognitive Dissonance (Stanford Univ. Press, 1957).

  • 127.

    Lewenstein, M., Nowak, A. & Latané, B. Statistical mechanics of social impact. Phys. Rev. A 45, 763–776 (1992).

    MathSciNet 
    CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar
     

  • 128.

    Morone, F. & Makse, H. A. Influence maximization in complex networks through optimal percolation. Nature 524, 65–68 (2015).

    CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar
     

  • 129.

    Haven, E. & Khrennikov, A. Quantum Social Science (Cambridge Univ. Press, 2013).

  • 130.

    Bettencourt, L. M. A., Cintrón-Arias, A., Kaiser, D. I. & Castillo-Chávez, C. The power of a good idea: quantitative modeling of the spread of ideas from epidemiological models. Phys. A Stat. Mech. Appl. 364, 513–536 (2006).

    Article 

    Google Scholar
     

  • 131.

    Daley, D. J. & Kendall, D. G. Epidemics and rumours. Nature 204, 1118 (1964).

    CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar
     

  • 132.

    Henrich, J. & McElreath, R. The evolution of cultural evolution. Evol. Anthropol. 12, 123–135 (2003).

    Article 

    Google Scholar
     

  • 133.

    Holyoak, K. J. & Thagard, P. Mental Leaps: Analogy in Creative Thought (MIT Press, 1995).

  • 134.

    Gigerenzer, G. From tools to theories: a heuristic of discovery in cognitive psychology. Psychol. Rev. 98, 254–267 (1991).

    Article 

    Google Scholar
     

  • 135.

    Dasgupta, A., Kumar, R. & Sivakumar, D. Social sampling. In Proc. 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 235 (ACM Press, 2012).

  • 136.

    Nettasinghe, B. & Krishnamurthy, V. ‘What do your friends think?’: efficient polling methods for networks using friendship paradox. IEEE Trans. Knowl. Data Eng. 33, 1291–1305 (2019).


    Google Scholar
     

  • 137.

    Kim, D., Gile, K. J., Guarino, H. & Mateu‐Gelabert, P. Inferring bivariate association from respondent‐driven sampling data. J. R. Stat. Soc. Ser. C 70, 415–433 (2021).

    MathSciNet 
    Article 

    Google Scholar
     

  • 138.

    Gile, K. J. & Handcock, M. S. Respondent-driven sampling: an assessment of current methodology. Sociol. Methodol. 40, 285–327 (2010).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 139.

    Kim, B. J. & Handcock, M. S. Population size estimation using multiple respondent-driven sampling surveys. J. Surv. Stat. Methodol. 9, 94–120 (2021).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 140.

    Berchenko, Y., Rosenblatt, J. D. & Frost, S. D. W. Modeling and analyzing respondent-driven sampling as a counting process. Biometrics 73, 1189–1198 (2017).

    MathSciNet 
    PubMed 
    MATH 
    Article 
    PubMed Central 

    Google Scholar
     

  • 141.

    Crawford, F. W., Wu, J. & Heimer, R. Hidden population size estimation from respondent-driven sampling: a network approach. J. Am. Stat. Assoc. 113, 755–766 (2018).

    MathSciNet 
    CAS 
    PubMed 
    PubMed Central 
    MATH 
    Article 

    Google Scholar
     

  • 142.

    Klingwort, J., Buelens, B. & Schnell, R. Capture–recapture techniques for transport survey estimate adjustment using permanently installed highway-sensors. Soc. Sci. Comput. Rev. https://doi.org/10.1177/0894439319874684 (2019).

  • 143.

    Tourangeau, R., Rips, L. T. & Rasinski, K. The Psychology of Survey Response (Cambridge Univ. Press, 2000).

  • 144.

    Batchelder, W. H. & Romney, A. K. Test theory without an answer key. Psychometrika 53, 71–92 (1988).

    MathSciNet 
    MATH 
    Article 

    Google Scholar
     

  • 145.

    Romney, A. K., Weller, S. C. & Batchelder, W. H. Culture as consensus: a theory of culture and informant accuracy. Am. Anthropol. 88, 313–338 (1986).

    Article 

    Google Scholar
     

  • 146.

    Prelec, D. A Bayesian truth serum for subjective data. Science 306, 462–466 (2004). Develops a Bayesian algorithm that incentivizes honest answers in a survey even if honesty is not independently verifiable.

    CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar
     

  • 147.

    Miller, N., Resnick, P. & Zeckhauser, R. Eliciting informative feedback: the peer-prediction method. Manage. Sci. 51, 1359–1373 (2005).

    Article 

    Google Scholar
     

  • 148.

    Baillon, A. Bayesian markets to elicit private information. Proc. Natl Acad. Sci. USA 114, 7958–7962 (2017).

    MathSciNet 
    CAS 
    PubMed 
    PubMed Central 
    MATH 
    Article 

    Google Scholar
     

  • 149.

    Cvitanić, J., Prelec, D., Riley, B. & Tereick, B. Honesty via choice-matching. Am. Econ. Rev. Insights 1, 179–192 (2019).

    Article 

    Google Scholar
     

  • 150.

    John, L. K., Loewenstein, G. & Prelec, D. Measuring the prevalence of questionable research practices with incentives for truth telling. Psychol. Sci. 23, 524–532 (2012).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 151.

    Prelec, D., Seung, H. S. & McCoy, J. A solution to the single-question crowd wisdom problem. Nature 541, 532–535 (2017). Addressing Galton’s original crowd wisdom problem (Nature 1907), this study provides a Bayesian criterion that identifies correct answers to a multiple choice questions even if the majority of polled individuals are wrong.

    CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar
     

  • 152.

    Foster, I., Ghani, R., Jarmin, R. S., Kreuter, F. & Lane, J. Big Data and Social Science: A Practical Guide to Methods and Tools (CRC Press, 2017). Provides practical guidance on combining methods and tools from computer science, statistics, and social science.

  • 153.

    Lane, J. Democratizing Our Data: A Manifesto (MIT Press, 2020).

  • 154.

    Haas, G. C., Trappmann, M., Keusch, F., Bähr, S. & Kreuter, F. Using geofences to collect survey data: lessons learned from the IAB-SMART study. Surv. Methods Insights Field https://doi.org/10.13094/SMIF-2020-00023 (2020).

  • 155.

    Christen, P., Ranbaduge, T. & Schnell, R. Linking Sensitive Data: Methods and Techniques for Practical Privacy-Preserving Information Sharing (Springer, 2020).

  • 156.

    Couper, M. P. Is the sky falling? New technology, changing media, and the future of surveys. Surv. Res. Methods 7, 145–156 (2013).


    Google Scholar
     

  • 157.

    Hill, C. et al. Big Data Meets Survey Science: A Collection of Innovative Methods (Wiley, 2020).

  • 158.

    Schnell, R. Survey-Interviews: Methoden standardisierter Befragungen (Springer, 2019).

  • 159.

    Olsson, H., Barman-Adhikari, A., Galesic, M., Hsu, H.-T. & Rice, E. Cognitive strategies for peer judgments. Preprint at https://doi.org/10.31219/osf.io/s3hxj (2021).

  • 160.

    van der Maas, H. L. J., Dalege, J. & Waldorp, L. The polarization within and across individuals: the hierarchical Ising opinion model. J. Complex Netw. 8, cnaa010 (2020).

    MathSciNet 
    Article 

    Google Scholar
     

  • 161.

    Fails, J. A. & Olsen, D. R. Interactive machine learning. in Proc. 8th International Conference on Intelligent User Interfaces 39 (ACM Press, 2003).

  • 162.

    Jiang, L., Liu, S. & Chen, C. Recent research advances on interactive machine learning. J. Vis. 22, 401–417 (2019).

    Article 

    Google Scholar
     

  • 163.

    Ware, M., Frank, E., Holmes, G., Hall, M. & Witten, I. H. Interactive machine learning: letting users build classifiers. Int. J. Hum. Comput. Stud. 55, 281–292 (2001).

    MATH 
    Article 

    Google Scholar
     

  • 164.

    Fortuna, P. & Nunes, S. A survey on automatic detection of hate speech in text. ACM Comput. Surv. 51, 1–30 (2018).

    Article 

    Google Scholar
     

  • 165.

    Garland, J., Ghazi-Zahedi, K., Young, J.-G., Hébert-Dufresne, L. & Galesic, M. Impact and dynamics of hate and counter speech online. Preprint at https://arxiv.org/abs/2009.08392 (2020).

  • 166.

    Leader Maynard, J. & Benesch, S. Dangerous speech and dangerous ideology: an integrated model for monitoring and prevention. Genocide Stud. Prev. 9, 70–95 (2016).

    Article 

    Google Scholar
     

  • 167.

    Abeliuk, A., Benjamin, D. M., Morstatter, F. & Galstyan, A. Quantifying machine influence over human forecasters. Sci. Rep. 10, 15940 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar
     

  • 168.

    Huber, D. J. et al. MATRICS: A system for human-machine hybrid forecasting of geopolitical events. In 2019 IEEE Intl Conf. Big Data 2028–2032 (IEEE, 2019).

  • 169.

    Morstatter, F. et al. SAGE: a hybrid geopolitical event forecasting system. In Intl Joint Conf. Artificial Intelligence 6557–6559 (2019).

  • 170.

    Evans, J. Social computing unhinged. J. Soc. Comput. 1, 1–13 (2020).

    Article 

    Google Scholar
     

  • 171.

    Wagner, C. et al. Measuring algorithmically infused societies. Nature https://doi.org/10.1038/s41586-021-03666-1 (2021).

  • 172.

    Hidalgo, C. A., Orghiain, D., Canals, J. A., De Almeida, F. & Martín, N. How Humans Judge Machines (MIT Press, 2021).

  • 173.

    Rahwan, I. et al. Machine behaviour. Nature 568, 477–486 (2019).

    CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar
     

  • 174.

    Lazer, D. et al. Meaningful measures of human society in the twenty-first century. Nature https://doi.org/10.1038/s41586-021-03660-7 (2021).

  • 175.

    Hofman, J. M. et al. Integrating explanation and prediction in computational social science. Nature https://doi.org/10.1038/s41586-021-03659-0 (2021).

  • 176.

    Trouille, L., Lintott, C. J. & Fortson, L. F. Citizen science frontiers: Efficiency, engagement, and serendipitous discovery with human-machine systems. Proc. Natl Acad. Sci. USA 116, 1902–1909 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 177.

    Outbreaks near me; https://outbreaksnearme.org/us/en-US/

  • 178.

    Schurz, M. et al. Toward a hierarchical model of social cognition: a neuroimaging meta-analysis and integrative review of empathy and theory of mind. Psychol. Bull. 147, 293–327 (2021).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 179.

    Adolphs, R. Cognitive neuroscience of human social behaviour. Nat. Rev. Neurosci. 4, 165–178 (2003).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 180.

    Feng, C. et al. Common brain networks underlying human social interactions: evidence from large-scale neuroimaging meta-analysis. Neurosci. Biobehav. Rev. 126, 289–303 (2021).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 181.

    Estes, W. K. Classification and Cognition (Oxford Univ. Press, 1994).

  • 182.

    Rieskamp, J. & Hoffrage, U. Inferences under time pressure: how opportunity costs affect strategy selection. Acta Psychol. 127, 258–276 (2008).

    Article 

    Google Scholar
     

  • 183.

    Ambady, N. & Rosenthal, R. Thin slices of expressive behavior as predictors of interpersonal consequences: a meta-analysis. Psychol. Bull. 111, 256–274 (1992).

    Article 

    Google Scholar
     

  • 184.

    Jern, A. & Kemp, C. A decision network account of reasoning about other people’s choices. Cognition 142, 12–38 (2015).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 185.

    Zacks, R. T. & Hasher, L. in Etc. Frequency Processing and Cognition Vol. 6, 21–36 (Oxford Univ. Press, 2002).

  • 186.

    Conrad, F. G., Brown, N. R. & Cashman, E. R. Strategies for estimating behavioural frequency in survey interviews. Memory 6, 339–366 (1998).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 187.

    Lynn, C. W. & Bassett, D. S. How humans learn and represent networks. Proc. Natl Acad. Sci. USA 117, 29407–29415 (2020).

    MathSciNet 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 188.

    Gagné, F. M. & Lydon, J. E. Bias and accuracy in close relationships: an integrative review. Pers. Soc. Psychol. Rev. 8, 322–338 (2004).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 189.

    Goel, S., Mason, W. & Watts, D. J. Real and perceived attitude agreement in social networks. J. Pers. Soc. Psychol. 99, 611–621 (2010).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 190.

    Dawes, R. M. Statistical criteria for establishing a truly false consensus effect. J. Exp. Soc. Psychol. 25, 1–17 (1989).

    Article 

    Google Scholar
     

  • 191.

    Nisbett, R. E. & Kunda, Z. Perception of social distributions. J. Pers. Soc. Psychol. 48, 297–311 (1985).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 192.

    Galesic, M., Olsson, H. & Rieskamp, J. Social sampling explains apparent biases in judgments of social environments. Psychol. Sci. 23, 1515–1523 (2012).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 193.

    Killworth, P. D. & Bernard, H. Informant accuracy in social network data. Hum. Organ. 35, 269–286 (1976).

    Article 

    Google Scholar
     

  • 194.

    Bernard, H. R. & Killworth, P. D. Informant accuracy in social network data II. Hum. Commun. Res. 4, 3–18 (1977).

    Article 

    Google Scholar
     

  • 195.

    Freeman, L. C., Romney, A. K. & Freeman, S. C. Cognitive structure and informant accuracy. Am. Anthropol. 89, 310–325 (1987).

    Article 

    Google Scholar
     

  • 196.

    Chang, L. & Krosnick, J. A. Measuring the frequency of regular behaviors: comparing the “typical week” to the “past week”. Sociol. Methodol. 33, 55–80 (2003).

    CAS 
    Article 

    Google Scholar
     

  • 197.

    Feld, S. L. & Carter, W. C. Detecting measurement bias in respondent reports of personal networks. Soc. Networks 24, 365–383 (2002).

    Article 
    ADS 

    Google Scholar
     

  • 198.

    Banerjee, A. V., Chandrasekhar, A. G., Duflo, E. & Jackson, M. O. Using gossips to spread information: theory and evidence from two randomized controlled trials. MIT Department of Economics Working Paper No. 14–15 http://www.ssrn.com/abstract=2425379 (2014).