Human social sensing is an untapped resource for computational social science - Nature.com

sanirbanir.blogspot.com

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

Rent or Buy

All prices are NET prices.

**Fig. 1: Human social sensing as a resource for computational social science.**

References

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).

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

Santa Fe Institute, Santa Fe, NM, USA

Mirta Galesic, Jonas Dalege, Henrik Olsson & Tamara van der Does
Complexity Science Hub Vienna, Vienna, Austria

Mirta Galesic
Vermont Complex Systems Center, University of Vermont, Burlington, VT, USA

Mirta Galesic
Harding Center for Risk Literacy, University of Potsdam, Potsdam, Germany

Mirta Galesic
Sol Price School of Public Policy, University of South California, Los Angeles, CA, USA

Wändi Bruine de Bruin
Department of Sociology, Purdue University, West Lafayette, IN, USA

Scott L. Feld
Joint Program in Survey Methodology, University of Maryland, Maryland, MD, USA

Frauke Kreuter
Ludwig Maximilians Universität München, München, Germany

Frauke Kreuter
Sloan School of Management, MIT, Cambridge, MA, USA

Drazen Prelec
Department of Economics, MIT, Cambridge, MA, USA

Drazen Prelec
Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA

Drazen Prelec
Department of Physics and Courant Institute of Mathematical Sciences, New York University, New York, NY, USA

Daniel L. Stein

Authors

Mirta Galesic

View author publications

You can also search for this author in PubMed Google Scholar
Wändi Bruine de Bruin

View author publications

You can also search for this author in PubMed Google Scholar
Jonas Dalege

View author publications

You can also search for this author in PubMed Google Scholar
Scott L. Feld

View author publications

You can also search for this author in PubMed Google Scholar
Frauke Kreuter

View author publications

You can also search for this author in PubMed Google Scholar
Henrik Olsson

View author publications

You can also search for this author in PubMed Google Scholar
Drazen Prelec

View author publications

You can also search for this author in PubMed Google Scholar
Daniel L. Stein

View author publications

You can also search for this author in PubMed Google Scholar
Tamara van der Does

View author publications

You can also search for this author in PubMed Google Scholar

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

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 (2021). https://ift.tt/3hrkOxY

Download citation

Received: 23 February 2021
Accepted: 17 May 2021
Published: 30 June 2021
DOI: https://doi.org/10.1038/s41586-021-03649-2

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.

Adblock test (Why?)

"social" - Google News
June 30, 2021 at 10:01PM
https://ift.tt/3h2Hl5n

Human social sensing is an untapped resource for computational social science - Nature.com
"social" - Google News
https://ift.tt/38fmaXp
https://ift.tt/2WhuDnP

Bagikan Berita Ini

Search