Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Human social sensing is an untapped resource for computational social science

Abstract

The ability to ‘sense’ the social environment and thereby to understand the thoughts and actions of others allows humans to fit into their social worlds, communicate and cooperate, and learn from others’ experiences. Here we argue that, through the lens of computational social science, this ability can be used to advance research into human sociality. When strategically selected to represent a specific population of interest, human social sensors can help to describe and predict societal trends. In addition, their reports of how they experience their social worlds can help to build models of social dynamics that are constrained by the empirical reality of human social systems.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Human social sensing as a resource for computational social science.
Fig. 2: Friendship paradox.

References

  1. 1.

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

  2. 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.

    Google Scholar 

  3. 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. 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. 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  Google Scholar 

  6. 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  ADS  Google Scholar 

  7. 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.

    Google Scholar 

  8. 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.

    Google Scholar 

  9. 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. 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  ADS  Google Scholar 

  11. 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.

    Google Scholar 

  12. 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. 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. 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  Google Scholar 

  15. 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  Google Scholar 

  16. 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).

    Google Scholar 

  17. 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  Google Scholar 

  18. 18.

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

  19. 19.

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

    MathSciNet  MATH  Google Scholar 

  20. 20.

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

    Google Scholar 

  21. 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).

    Google Scholar 

  22. 22.

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

  23. 23.

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

  24. 24.

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

  25. 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. 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).

    Google Scholar 

  27. 27.

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

    Google Scholar 

  28. 28.

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

    Google Scholar 

  29. 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.

    Google Scholar 

  30. 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  Google Scholar 

  31. 31.

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

    PubMed  PubMed Central  Google Scholar 

  32. 32.

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

    MathSciNet  Google Scholar 

  33. 33.

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

    Google Scholar 

  34. 34.

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

  35. 35.

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

  36. 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. 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).

    Google Scholar 

  38. 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. 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).

    Google Scholar 

  40. 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. 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).

    Google Scholar 

  42. 42.

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

  43. 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).

    Google Scholar 

  44. 44.

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

    Google Scholar 

  45. 45.

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

    Google Scholar 

  46. 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. 47.

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

    PubMed  PubMed Central  ADS  Google Scholar 

  48. 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  ADS  Google Scholar 

  49. 49.

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

  50. 50.

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

    Google Scholar 

  51. 51.

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

    PubMed  Google Scholar 

  52. 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  Google Scholar 

  53. 53.

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

    Google Scholar 

  54. 54.

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

    Google Scholar 

  55. 55.

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

    Google Scholar 

  56. 56.

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

    Google Scholar 

  57. 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  Google Scholar 

  58. 58.

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

    MathSciNet  Google Scholar 

  59. 59.

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

  60. 60.

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

  61. 61.

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

    CAS  PubMed  Google Scholar 

  62. 62.

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

  63. 63.

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

  64. 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).

    Google Scholar 

  65. 65.

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

  66. 66.

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

  67. 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  Google Scholar 

  68. 68.

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

    Google Scholar 

  69. 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  Google Scholar 

  70. 70.

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

    CAS  PubMed  Google Scholar 

  71. 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  ADS  Google Scholar 

  72. 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  Google Scholar 

  73. 73.

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

    Google Scholar 

  74. 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.

    Google Scholar 

  75. 75.

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

    PubMed  ADS  Google Scholar 

  76. 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. 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  Google Scholar 

  78. 78.

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

  79. 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  ADS  Google Scholar 

  80. 80.

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

    Google Scholar 

  81. 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  ADS  Google Scholar 

  82. 82.

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

    Google Scholar 

  83. 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  Google Scholar 

  84. 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  Google Scholar 

  85. 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  Google Scholar 

  86. 86.

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

  87. 87.

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

    ADS  Google Scholar 

  88. 88.

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

    CAS  ADS  Google Scholar 

  89. 89.

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

    CAS  PubMed  Google Scholar 

  90. 90.

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

  91. 91.

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

  92. 92.

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

    Google Scholar 

  93. 93.

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

    Google Scholar 

  94. 94.

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

    CAS  Google Scholar 

  95. 95.

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

    Google Scholar 

  96. 96.

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

    PubMed  Google Scholar 

  97. 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  Google Scholar 

  98. 98.

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

    PubMed  Google Scholar 

  99. 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).

    Google Scholar 

  100. 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. 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  ADS  Google Scholar 

  102. 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  ADS  Google Scholar 

  103. 103.

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

    PubMed  Google Scholar 

  104. 104.

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

    Google Scholar 

  105. 105.

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

  106. 106.

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

    MATH  Google Scholar 

  107. 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. 108.

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

    CAS  PubMed  ADS  Google Scholar 

  109. 109.

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

  110. 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).

    Google Scholar 

  111. 111.

    Marr, D. Vision (Freeman, 1982).

  112. 112.

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

  113. 113.

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

    PubMed  Google Scholar 

  114. 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. 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  Google Scholar 

  116. 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  Google Scholar 

  117. 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.

    Google Scholar 

  118. 118.

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

    Google Scholar 

  119. 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.

    ADS  Google Scholar 

  120. 120.

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

    MathSciNet  MATH  ADS  Google Scholar 

  121. 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.

    Google Scholar 

  122. 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  Google Scholar 

  123. 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  Google Scholar 

  124. 124.

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

  125. 125.

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

    Google Scholar 

  126. 126.

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

  127. 127.

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

    MathSciNet  CAS  PubMed  ADS  Google Scholar 

  128. 128.

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

    CAS  PubMed  ADS  Google Scholar 

  129. 129.

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

  130. 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).

    Google Scholar 

  131. 131.

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

    CAS  PubMed  ADS  Google Scholar 

  132. 132.

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

    Google Scholar 

  133. 133.

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

  134. 134.

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

    Google Scholar 

  135. 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. 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. 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  Google Scholar 

  138. 138.

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

    PubMed  PubMed Central  Google Scholar 

  139. 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  Google Scholar 

  140. 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  Google Scholar 

  141. 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  Google Scholar 

  142. 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. 143.

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

  144. 144.

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

    MathSciNet  MATH  Google Scholar 

  145. 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).

    Google Scholar 

  146. 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  ADS  Google Scholar 

  147. 147.

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

    Google Scholar 

  148. 148.

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

    MathSciNet  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  149. 149.

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

    Google Scholar 

  150. 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  Google Scholar 

  151. 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  ADS  Google Scholar 

  152. 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. 153.

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

  154. 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. 155.

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

  156. 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. 157.

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

  158. 158.

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

  159. 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. 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  Google Scholar 

  161. 161.

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

  162. 162.

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

    Google Scholar 

  163. 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  Google Scholar 

  164. 164.

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

    Google Scholar 

  165. 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. 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).

    Google Scholar 

  167. 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  ADS  Google Scholar 

  168. 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. 169.

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

  170. 170.

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

    Google Scholar 

  171. 171.

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

  172. 172.

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

  173. 173.

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

    CAS  PubMed  ADS  Google Scholar 

  174. 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. 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. 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  Google Scholar 

  177. 177.

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

  178. 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  Google Scholar 

  179. 179.

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

    CAS  PubMed  Google Scholar 

  180. 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  Google Scholar 

  181. 181.

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

  182. 182.

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

    Google Scholar 

  183. 183.

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

    Google Scholar 

  184. 184.

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

    PubMed  PubMed Central  Google Scholar 

  185. 185.

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

  186. 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  Google Scholar 

  187. 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  Google Scholar 

  188. 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  Google Scholar 

  189. 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  Google Scholar 

  190. 190.

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

    Google Scholar 

  191. 191.

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

    CAS  PubMed  Google Scholar 

  192. 192.

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

    PubMed  Google Scholar 

  193. 193.

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

    Google Scholar 

  194. 194.

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

    Google Scholar 

  195. 195.

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

    Google Scholar 

  196. 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  Google Scholar 

  197. 197.

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

    ADS  Google Scholar 

  198. 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).

Download references

Acknowledgements

We thank F. Gerdon, J. Foster, R. Kurvers, M. Schierholz, P. Schenk, T. Wallsten, and C. Wagner for comments on an earlier version of the manuscript, as well as our many collaborators for their contributions to this work. M.G., H.O., T.v.d.D., J.D., W.B.d.B., and D.P. were supported in part by grants from the National Science Foundation (M.G.: DRMS-1757211; H.O., M.G., and J.D.: BCS-1918490; M.G., H.O., and T.v.d.D.: DRMS-1949432; H.O., M.G., W.B.d.B., and D.P.: MMS-2019982), M.G., T.v.d.D., and D.L.S. were supported in part by a grant from the National Institute of Food and Agriculture (NIFA 2018-67023-27677), and J.D. was supported in part by an EU Horizon 2020 Marie Curie Global Fellowship (no. 889682).

Author information

Affiliations

Authors

Contributions

All authors contributed equally to the writing of this Perspective.

Corresponding author

Correspondence to Mirta Galesic.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Jacob Foster, Ralf Kurvers and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Galesic, M., Bruine de Bruin, W., Dalege, J. et al. Human social sensing is an untapped resource for computational social science. Nature 595, 214–222 (2021). https://doi.org/10.1038/s41586-021-03649-2

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing