Homophily and contagion are generically confounded in observational social network studies
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The authors consider processes on social networks that can potentially involve three factors: homophily, or the formation of social ties due to matching individual traits; social contagion, also known as social influence; and the causal effect of an individual’s covariates on his or her behavior or other measurable responses. The authors show that generically, all of these are confounded with each other. Distinguishing them from one another requires strong assumptions on the parametrization of the social process or on the adequacy of the covariates used (or both). In particular the authors demonstrate, with simple examples, that asymmetries in regression coefficients cannot identify causal effects and that very simple models of imitation (a form of social contagion) can produce substantial correlations between an individual’s enduring traits and his or her choices, even when there is no intrinsic affinity between them. The authors also suggest some possible constructive responses to these results.
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One solution to latent homophily bias is to model tie formation and dissolution explicitly, for example by measuring and conditioning on tie-forming characteristics of either or both individuals.
“One solution to this problem is to model tie formation (and dissolution) explicitly (Shalizi & Thomas 2011). In the model of Figure 12, this can be accomplished by measuring and conditioning on the tie-forming characteristics of individual i, Ui, or individual j, Uj, or both.”
From Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable (2014) · cites this paper as Shalizi & Thomas 2011
In “Contagion Effects Are Nonparametrically Unidentifiable” (page unavailable), the paper says that making the traits influencing tie formation observable can remove the confounding path, supporting the proposed conditioning on tie-forming characteristics. But it does not present explicitly modeling tie formation and dissolution alone as a solution: it says allowing ties to vary over time does not help, and describes successful explicit models as also requiring strong assumptions and adequate observed covariates. (LLM, conf=0.84)
Experimental — accuracy not yet measuredShalizi and Thomas showed that latent homophily bias is an example of endogenous selection bias, with the social tie as a pre-treatment collider.
“Shalizi & Thomas (2011) recently showed that latent homophily bias constitutes a previously unrecognized example of endogenous selection bias, where the social tie itself plays the role of the pre-treatment collider.”
From Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable (2014) · cites this paper as Shalizi & Thomas 2011
Network studies of peer influence and social contagion suffer from a stubborn challenge of disambiguating such effects from homophily among peers and within communities.
“network studies of peer influence and social contagion suffer from a stubborn challenge of disambiguating such effects from homophily among peers and within communities (Christakis and Fowler, 2007; Lyons, 2011; Shalizi and Thomas, 2011).”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Shalizi and Thomas, 2011
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BackgroundResultDistinguishing influence-based contagion from homophily-driven diffusion in dynamic networks
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“Statistical techniques such as permutation tests and matched sampling [5] help control for confounds, but ultimately cannot resolve this fundamental problem [39].”
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“For instance, network studies of peer influence and social contagion suffer from a stubborn challenge of disambiguating such effects from homophily among peers and within communities (Christakis and Fowler, 2007; Lyons, 2011; Shalizi and Thomas, 2011).”
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BackgroundVirality Prediction and Community Structure in Social Networks
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“Separating social contagion and homophily is difficult [41, 42], and we interpret complex contagion broadly to include homophily; we focus on how both social reinforcement and homophily effects collectively boost the trapping of memes within dense communities, not on the distinctions between them.”
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Network analysis reveals open forums and echo chambers in social media discussions of climate change
Williams, H. T. P., McMurray, J. R., Kurz, T., & Hugo Lambert, F. (2015) · Global Environmental Change · doi:10.1016/j.gloenvcha.2015.03.006 · Cited by 631 · see citing works
“Although it can be difficult to distinguish which mechanism has operated to cause homophily in purely observational studies (Shalizi and Thomas, 2011), experimental approaches have been used to demonstrate online peer influence affecting individual attitudes and behaviours, including musical preferences (Salganik et a…”
BackgroundAbbasi, M. A., Liu, H., & Zafarani, R. (2014) · Encyclopedia of Social Network Analysis and Mining. 2nd Ed. · doi:10.1007/978-1-4939-7131-2_101143 · Cited by 614
Network Effects and Social Inequality
DiMaggio, P., & Garip, F. (2012) · Annual Review of Sociology · doi:10.1146/annurev.soc.012809.102545 · Cited by 575 · see citing works
“…out others whose practices they wish to emulate, in which case the intent to adopt a practice produces rather than is caused by networks (Mouw 2002, Shalizi & Thomas 2011).3 Researchers have addressed 3Few studies have used longitudinal network data to compare the effects of influence and…”
BackgroundInformation diffusion and external influence in networks
Myers, S. A., Zhu, C., & Leskovec, J. (2012) · Knowledge Discovery and Data Mining · doi:10.1145/2339530.2339540 · Cited by 548 · see citing works
Social Capital: Maturation of a Field of Research
Kwon, S.-W., & Adler, P. (2014) · Academy of Management Review · doi:10.5465/amr.2014.0210 · Cited by 546 · see citing works
“As a result, it is quite possible that some effects attributed to social capital are, in reality, due to selection (Mouw, 2003, 2006; Shalizi & Thomas, 2011).”
BackgroundMeasuring Emotional Contagion in Social Media
Ferrara, E., & Yang, Z. (2015) · PLoS ONE · doi:10.1371/journal.pone.0142390 · Cited by 528 · see citing works
Social selection and peer influence in an online social network
Lewis, K., Gonzalez, M., & Kaufman, J. (2011) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.1109739109 · Cited by 507 · see citing works
Friendship prediction and homophily in social media
Aiello, L., Barrat, A., Schifanella, R., Cattuto, C., Markines, B., & Menczer, F. (2012) · TWEB · doi:10.1145/2180861.2180866 · Cited by 447 · see citing works
The structure of online diffusion networks
Goel, S., Watts, D., & Goldstein, D. G. (2012) · ACM Conference on Economics and Computation · doi:10.1145/2229012.2229058 · Cited by 412 · see citing works
“Although in the latter case it is possible that apparent influence can be accounted for simply by homophily [Aral et al. 2009; Shalizi and Thomas 2011], our inferred diffusion trees nevertheless provide an effective upper bound on the actual diffusion taking place via the respective underlying networks.”
BackgroundMeasuring Large-Scale Social Networks with High Resolution
Stopczynski, A., Sekara, V., Sapieżyński, P., Cuttone, A., Madsen, M. M., Larsen, J. E., & Lehmann, S. (2014) · PLoS ONE · doi:10.1371/journal.pone.0095978 · Cited by 399 · see citing works
“We can, for example, divide participants into sub-populations and expose them to distinct stimuli, addressing the topic of causality as well as confounding factors both of which have proven problematic [64,121] for the current state-of-the-art [122,123].”
BackgroundAttributed Network Embedding for Learning in a Dynamic Environment
Li, J., Dani, H., Hu, X., Tang, J.-L., Chang, Y., & Liu, H. (2017) · International Conference on Information and Knowledge Management · doi:10.1145/3132847.3132919 · Cited by 399 · see citing works
“It has been widely studied and received that there exhibits a strong correlation among the aributes of linked nodes [27, 29].”
BackgroundThe Consequences of the Internet for Politics
Farrell, H. (2012) · Annual Review of Political Science · doi:10.1146/annurev-polisci-030810-110815 · Cited by 387 · see citing works
“In part, this reflects fundamental methodological problems in distinguishing between homophily and social influence using standard data (Shalizi & Thomas 2011), and in part, genuine ambiguity in research results.”
BackgroundTie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment
Aral, S., & Walker, D. (2014) · Management Science · doi:10.1287/mnsc.2014.1936 · Cited by 382 · see citing works
“Moreover, homophily in unobserved attributes (latent homophily) that may be indicated by the very existence of a relationship of peers with a common friend (see Shalizi and Thomas 2011) will be equally represented in recipient and non-recipient peer groups.”
BackgroundThe Book of Why: The New Science of Cause and Effect†
Goldberg, L. R. (2019) · Quantitative finance (Print) · doi:10.1080/14697688.2019.1655928 · Cited by 354 · see citing works
“It is also difficult to comprehend its nuanced refuta-tions by Lyons (2011) and by Shalizi and Thomas (2011).”
BackgroundSocial Network Assessments and Interventions for Health Behavior Change: A Critical Review
Latkin, C., & Knowlton, A. (2015) · Behavioral Medicine · doi:10.1080/08964289.2015.1034645 · Cited by 351 · see citing works
“However, in randomized clinical trials of social network interventions, the impact of interventions may be underestimated.(44,47) Social diffusion can also be viewed as contamination or interference if diffusion occurs from an experimental network into a control group network.”
BackgroundMuller, E., & Peres, R. (2019) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2018.05.003 · Cited by 334 · see citing works
Exercise contagion in a global social network
Aral, S., & Nicolaides, C. (2017) · Nature Communications · doi:10.1038/ncomms14753 · Cited by 329 · see citing works
“However, observational techniques struggle to overcome the confounding effects of unobservable factors 20 , while experimental studies, which provide more robust causal inference, are complex, difficult to implement and therefore more rare.”
Elwert, F. (2013) · Handbooks of Sociology and Social Research · doi:10.1007/978-94-007-6094-3_13 · Cited by 325 · see citing works
Digital Footprints: Opportunities and Challenges for Online Social Research
Golder, S. A., & Macy, M. (2014) · Annual Review of Sociology · doi:10.1146/annurev-soc-071913-043145 · Cited by 321 · see citing works
“Aral et al. (2009) reviewed numerous statistical techniques that have been proposed to tease these processes apart using observational data and concluded that none are sufficient, a conclusion also reached by Shalizi & Thomas (2011).”
BackgroundSocial Networks and Political Participation
Campbell, D. E. (2013) · Annual Review of Political Science · doi:10.1146/annurev-polisci-033011-201728 · Cited by 305 · see citing works
Evidence of complex contagion of information in social media: An experiment using Twitter bots
Mønsted, B., Sapiezynski, P., Ferrara, E., & Lehmann, S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0184148 · Cited by 302 · see citing works
Social influence in social advertising: evidence from field experiments
Bakshy, E., Eckles, D., Yan, R., & Rosenn, I. (2012) · ACM Conference on Economics and Computation · doi:10.1145/2229012.2229027 · Cited by 295 · see citing works
Happiness Is Assortative in Online Social Networks
Bollen, J., Gonçalves, B., Ruan, G.-C., & Mao, H.-N. (2011) · Artificial Life · doi:10.1162/artl_a_00034 · Cited by 294 · see citing works
Dynamical Systems on Networks: A TutorialPreprint
Porter, M., & Gleeson, J. (2014) · arXiv.org · doi:10.48550/arxiv.1403.7663 · Cited by 288 · see citing works
“Even discerning whether or not genuine social influence is occurring in a network is extremely challenging [211], and we find it helpful (for, e.”
BackgroundFood insecurity as a driver of obesity in humans: The insurance hypothesis
Cardel, M. I., Pavela, G., Dhurandhar, E., & Allison, D. B. (2016) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001345 · Cited by 284
Digital Emotion Contagion.Preprint
Goldenberg, A., & Gross, J. (2019) · Trends in Cognitive Sciences · doi:10.31219/osf.io/53bdu · Cited by 282 · see citing works
“However, both methods cannot fully guarantee that we are able to capture contagion [65].”
BackgroundInfectious Disease Modeling of Social Contagion in Networks
Hill, A., Rand, D., Nowak, M., & Christakis, N. (2010) · PLoS Comput. Biol. · doi:10.1371/journal.pcbi.1000968 · Cited by 282 · see citing works
“It has recently been suggested that certain, particular types of latent homophily, in which an unobservable trait influences both which friends one chooses and current and future behavior, may be impossible to distinguish from contagion in observational studies and hence may bias estimates of contagion and homophily […”
BackgroundPerkins, J. M., Subramanian, S., & Christakis, N. (2014) · Social Science & Medicine (1967) · doi:10.1016/j.socscimed.2014.08.019 · Cited by 258 · see citing works
“…usual sorts of constraints affecting observational studies (e.g. the observed association not necessarily reflecting causal effects), plus other limitations that are distinctive to network data settings (Aral et al., 2009; Christakis and Fowler, 2007, 2013; Manski, 1993; Shalizi and Thomas, 2011).”
BackgroundStochastic Actor-Oriented Models for Network Dynamics
Snijders, T., & Pickup, M. (2017) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-060116-054035 · Cited by 253 · see citing works
“This methodology does not pretend to yield causal conclusions by virtue of the statistical analysis; Shalizi and Thomas (2011) argued cogently that this is impossible in panel studies.”
MethodDunn, A., Leask, J., Zhou, X.-J., Mandl, K., & Coiera, E. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4343 · Cited by 248 · see citing works
“It is important to note that the study design we used precluded conclusions about what proportions of negative opinions expressed in the period were the consequence of exposure (contagion of opinions), the consequence of users creating connections to other users who already hold similar opinions (homophily), or if oth…”
Osgood, D., Ragan, D. T., Wallace, L. N., Gest, S. D., Feinberg, M., & Moody, J. (2013) · Journal of Research on Adolescence · doi:10.1111/jora.12059 · Cited by 242 · see citing works
“Limitations and Future Directions Shalizi and Thomas (2011) argued that network analyses of social influence are susceptible to bias due to selection of friends who are similar on unmeasured variables.”
BackgroundGreen, B., Horel, T., & Papachristos, A. V. (2017) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2016.8245 · Cited by 228 · see citing works
Trading on Twitter: Using Social Media Sentiment to Predict Stock Returns
Sul, H., Dennis, A., & Yuan, L.-Y. (2017) · Decision Sciences · doi:10.1111/deci.12229 · Cited by 228 · see citing works
Graph Condensation for Graph Neural NetworksPreprint
Jin, W., Zhao, L., Zhang, S., Liu, Y., Tang, J.-L., & Shah, N. (2021) · International Conference on Learning Representations · doi:10.48550/arxiv.2110.07580 · Cited by 225 · see citing works
Wang, D. J., & Soule, S. (2012) · American Journal of Sociology · doi:10.1086/664685 · Cited by 222 · see citing works
Social-aware D2D communications: qualitative insights and quantitative analysis
Li, Y., Wu, T., Hui, P., Jin, D.-P., & Chen, S. (2014) · IEEE Communications Magazine · doi:10.1109/mcom.2014.6829957 · Cited by 219 · see citing works
“Allocating more spectrum and energy resources to users with strong ties can increase the peer discovery ratio, help avoid congestion, improve spectral efficiency, and eventually increase the overall throughput and coverage of the D2D underlaying cellular network.”
BackgroundSocial Ties and User Content Generation: Evidence from Flickr
Zeng, X.-H., & Wei, L.-Y. (2013) · Information systems research · doi:10.1287/isre.1120.0464 · Cited by 198 · see citing works
“For example, homophily occurs when unobserved tastes and preferences drive the formation of social ties; thus, the linked user dyads tend to be more similar in their uploaded photos than the average users (Aral et al. 2009, McPherson 2001, Shalizi and Thomas 2011).”
BackgroundThe influence of social relationships on pro-environment behaviors
Videras, J., Owen, A. L., Conover, E., & Wu, S. (2012) · Journal of Environmental Economics and Management · doi:10.1016/j.jeem.2011.07.006 · Cited by 195 · see citing works
“For example, Shalizi and Thomas [35] show that social influence effects and assertive-mixing (self-selection) effects are typically confounded and that the estimation strategies Christakis and Fowler have adopted 4 might not suffice to separate between these effects.”
BackgroundInfluentialSocial Media and the Science of Health Behavior
Centola, D. (2013) · Circulation · doi:10.1161/circulationaha.112.101816 · Cited by 193 · see citing works
Dickison, M., Magnani, M., & Rossi, L. (2016) · Cambridge University Press eBooks · doi:10.1017/cbo9781139941907 · Cited by 188 · see citing works
The spread of evidence-poor medicine via flawed social-network analysis
Lyons, R. (2011) · Statistics, Politics, and Policy · doi:10.2202/2151-7509.1024 · Cited by 182 · see citing works
Crowd sourcing disaster management: The complex nature of Twitter usage in Padang IndonesiaPreprint
Carley, K. M., Malik, M. M., Landwehr, P., Pfeffer, J., & Kowalchuck, M. (2016) · SSRN Electronic Journal · doi:10.1016/j.ssci.2016.04.002 · Cited by 177 · see citing works
Latent Homophily or Social Influence? An Empirical Analysis of Purchase Within a Social Network
Ma, L.-Y., Krishnan, R., & Montgomery, A. (2015) · Management Sciences · doi:10.1287/mnsc.2014.1928 · Cited by 177 · see citing works
“2009), but the issue of latent homophily is more challenging (Shalizi and Thomas 2011).”
BackgroundValente, T. W., & Pitts, S. (2016) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-031816-044528 · Cited by 207 · see citing works
Jadidi, M., Karimi, F., Lietz, H., & Wagner, C. (2017) · Advances in Complex Systems · doi:10.1142/s0219525917500114 · Cited by 163 · see citing works
“[ 22, 53]”
BackgroundFour Ways We Can Improve Policy Diffusion Research
Gilardi, F. (2015) · State Politics & Policy Quarterly · doi:10.1177/1532440015608761 · Cited by 211 · see citing works
Miller, G., & Mobarak, A. (2015) · Marketing science (Providence, R.I.) · doi:10.1287/mksc.2014.0845 · Cited by 162 · see citing works
“Quantifying these social network effects is empirically challenging because it is difficult to distinguish social learning from common unobservable shocks faced by network members (Manski 1993, Bemmaor 1994, Aral et al. 2009, Shalizi and Thomas 2011).”
BackgroundBusiness Intelligence and Analytics: Research Directions
Lim, E.-P., Chen, H.-C., & Chen, G.-Q. (2013) · TMIS · doi:10.1145/2407740.2407741 · Cited by 160 · see citing works
Causal Inference for Social Network Data
Ogburn, E., Sofrygin, O., Díaz, I., & van der Laan, M. V. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2022.2131557 · Cited by 159 · see citing works
“These methods have come under considerable criticism from the statistical community (Cohen-Cole and Fletcher 2008; Lyons 2011; Shalizi and Thomas 2011), in part because these statistical models are not equipped to deal with dependence across individuals (Ogburn and VanderWeele 2014).”
BackgroundMethodInfluentialEesley, C., & Wang, Y.-B. (2017) · doi:10.1016/j.respol.2017.01.010 · Cited by 157
“Yet our understanding of social influence is still incomplete (Sørensen and Fassiotto, 2011; Shalizi and Thomas, 2011).”
BackgroundPredicting Adoption Probabilities in Social Networks
Fang, X., Hu, P. J.-H., Li, Z.-P., & Tsai, W. (2013) · Information systems research · doi:10.1287/isre.1120.0461 · Cited by 153 · see citing works
“1990), represent an important force affecting individuals’ adoption behaviors in a social network (Ibarra and Andrews 1993, Leenders 2002, Bruyn and Lilien 2008, Shalizi and Thomas 2011).”
BackgroundEngineering social contagions: Optimal network seeding in the presence of homophily
Aral, S., Muchnik, L., & Sundararajan, A. (2013) · Network Science · doi:10.1017/nws.2013.6 · Cited by 152 · see citing works
“How different types of consumers are distributed over the network can have a dramatic impact on the diffusion process and the likely effects of seeding (Aral et al., 2009; McPherson et al., 2001; Noel & Nyhan, 2011; Shalizi & Thomas, 2011).”
BackgroundTwitter reciprocal reply networks exhibit assortativity with respect to happiness
Bliss, C. A., Kloumann, I. M., Harris, K. D., Danforth, C., & Dodds, P. (2011) · Journal of Computer Science · doi:10.48550/arxiv.1112.1010 · Cited by 149
“The authors’ additional assertion of contagion, however, has been criticized primarily on the basis of the difficulties to be found in distinguishing these phenomena from homophily [26–28].”
BackgroundChoosing your network: social preferences in an online health community.
Centola, D., & van de Rijt, A. (2015) · Social Science & Medicine (1967) · doi:10.1016/j.socscimed.2014.05.019 · Cited by 148 · see citing works
“Each of these methods is designed to solve problems of causality and identification that are caused by uncontrolled factors such as endogeneity, unobserved heterogeneity, and exogenous influence (Aral et al., 2009; Shalizi and Thomas, 2011).”
MethodAzoulay, P., Liu, C. C., & Stuart, T. E. (2017) · American Journal of Sociology · doi:10.1086/689890 · Cited by 146
Sensitivity analysis for contagion effects in social networks
VanderWeele, T. (2011) · Sociological Methods & Research · doi:10.1177/0049124111404821 · Cited by 144 · see citing works
Estimating peer effects in networks with peer encouragement designs
Eckles, D., Kizilcec, R. F., & Bakshy, E. (2016) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.1511201113 · Cited by 141 · see citing works
“581–590, and discussion (2013) 32(4):597–599.”
BackgroundAttributed graph models: modeling network structure with correlated attributes
Pfeiffer, J. J., Moreno, S., Fond, T. L., Neville, J., & Gallagher, B. (2014) · The Web Conference · doi:10.1145/2566486.2567993 · Cited by 140
“…has considered the root cause of this correlation and distinguished between social influence , which is the tendency for linked individuals to adopt the characteristics of their neighbors, and homophily , where links are created based on the attribute similarities of the individuals [10, 15, 22].”
BackgroundNoel, H., & Nyhan, B. (2010) · Soc. Networks · doi:10.1016/j.socnet.2011.05.003 · Cited by 138 · see citing works
Shoham, D., Tong, L.-P., Lamberson, P. J., Auchincloss, A., Zhang, J., Dugas, L. R., Kaufman, J., Cooper, R., & Luke, A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0039795 · Cited by 136 · see citing works
“Notable critiques of the social network ‘‘contagion’’ hypothesis have appeared in academic [23,24,25,26] and popular [27] literatures.”
BackgroundInfluentialIs Fraud Contagious? Co-Worker Influence on Misconduct by Financial AdvisorsPreprint
Dimmock, S. G., Gerken, W., & Graham, N. P. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2577311 · Cited by 133 · see citing works
A natural experiment of social network formation and dynamics
Phan, T., & Airoldi, E. (2015) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.1404770112 · Cited by 130 · see citing works
“Related literature focuses on incentives, aiming at separating influence from selection effects, a task for which negative results exist in general (27, 28), using randomized experiments (29–32) and strategies tailored to specific applications (17, 33).”
BackgroundMethodZhang, W., Chintagunta, P., & Kalwani, M. U. (2021) · Journal of Marketing · doi:10.1177/0022242920985784 · Cited by 129 · see citing works
“Compared with using observational data, running experiments such as peer encouragement designs can effectively avoid the presence of confounding due to homophily and common external causes (e.g., Manski 1993; Shalizi and Thomas 2011).”
MethodYoung, J. T. N., Rebellon, C. J., Barnes, J. C., & Weerman, F. (2014) · Criminology · doi:10.1111/1745-9125.12029 · Cited by 129 · see citing works
“As Shalizi and Thomas (2011: 213) observed, “even when the true causal effect of the homophilous trait is zero, the trait can still act as a strong predictor of the outcome of interest.””
The acquisition of perceived descriptive norms as social category learning in social networks
Kashima, Y., Wilson, S., Lusher, D., Pearson, L., & Pearson, C. (2013) · Soc. Networks · doi:10.1016/j.socnet.2013.06.002 · Cited by 128 · see citing works
“Although it is difficult to separate contagion and homophily (see e.g., Shalizi & Thomas, 2011; also see Cohen-Cole & Fletcher, 2008; Fowler & Christakis, 2008b), it is well established by now that people with similar attributes tend to have a network tie.”
BackgroundSankar, A., Zhang, X., Krishnan, A., & Han, J. (2020) · Web Search and Data Mining · doi:10.1145/3336191.3371811 · Cited by 126 · see citing works
“social homophily and temporal influence are fundamentally confounded in observational studies [37].”
BackgroundPowell, K., Wilcox, J., Clonan, A., Bissell, P., Preston, L., Peacock, M., & Holdsworth, M. (2015) · BMC Public Health · doi:10.1186/s12889-015-2314-0 · Cited by 123 · see citing works
“As Shalizi and Thomas argue, contagion and selection processes are mutually influential, since our decisions about who we associate with are influenced by the ways in which these people shape our behaviour and vice versa [73].”
BackgroundSuicide Contagion: A Systematic Review of Definitions and Research Utility
Cheng, Q., Li, H., Silenzio, V., & Caine, E. (2014) · PLoS ONE · doi:10.1371/journal.pone.0108724 · Cited by 122 · see citing works
“Thus, affiliation would seem to be complementary to imitation, consistent with recent developments in social network research [64,65].”
ResultThe Structure of Online Activism
Lewis, K., Gray, K., & Meierhenrich, J. (2014) · Sociological Science · doi:10.15195/v1.a1 · Cited by 121 · see citing works
Gender homophily from spatial behavior in a primary school: A sociometric study
Stehlé, J., Charbonnier, F., Picard, T., Cattuto, C., & Barrat, A. (2013) · Soc. Networks · doi:10.1016/j.socnet.2013.08.003 · Cited by 119
“Unless panel data on social relationships and individual traits are available it is impossible to disentangle the above effects (Kossinets and Watts, 2009; Shalizi and Thomas, 2010), with the exception of”
BackgroundShore, J. C., Bernstein, E. S., & Lazer, D. M. J. (2015) · Organization Science · doi:10.1287/orsc.2015.0980 · Cited by 109 · see citing works
“Indeed it is essentially impossible to identify the independent effects of structure in field work (Shalizi and Thomas, 2011), requiring experimental methods to untangle its consequences.”
The dynamics of health behavior sentiments on a large online social network
Salathé, M., Vu, D. Q., Khandelwal, S., & Hunter, D. R. (2013) · EPJ Data Science · doi:10.1140/epjds16 · Cited by 116 · see citing works
“The lack of detailed information about the users prohibits us from assessing manifest homophily, and our analysis is thus subject to the problem of latent homophily which is generally confounded with contagion (8).”
InfluentialPredicting Individual Behavior with Social Networks
Goel, S., & Goldstein, D. G. (2014) · Marketing science (Providence, R.I.) · doi:10.1287/mksc.2013.0817 · Cited by 114 · see citing works
McDermott, R., Fowler, J., & Christakis, N. (2013) · Social forces; a scientific medium of social study and interpretation · doi:10.1093/sf/sot096 · Cited by 112 · see citing works
“This lagged model is specifically recommended by Shalizi and Thomas (2010) and Vanderweele (2011) as an alternative to previous models that focused on contemporaneous effects because it helps to better control for homophily (the tendency of people to form social ties with others who have similar…”
InfluentialMulti-Stage Complex Contagions
Melnik, S., Ward, J. A., Gleeson, J., & Porter, M. (2011) · Chaos · doi:10.1063/1.4790836 · Cited by 112 · see citing works
McMillan, C., Felmlee, D. H., & Osgood, D. (2018) · Soc. Networks · doi:10.1016/j.socnet.2018.05.008 · Cited by 111 · see citing works
“Finally, teasing apart the effect of peer influence from friend selection is difficult, and since we cannot account for the selection of friends on a variety of latent variables, we do not argue that our results imply causation (Shalizi and Thomas, 2011).”
BackgroundTreatment and Spillover Effects Under Network Interference
Leung, M. P. (2020) · Review of Economics and Statistics · doi:10.1162/rest_a_00818 · Cited by 109 · see citing works
A personal network approach to the study of immigrant structural assimilation and transnationalism
Vacca, R., Solano, G., Lubbers, M., Molina, J., & McCarty, C. (2016) · Soc. Networks · doi:10.1016/j.socnet.2016.08.007 · Cited by 109 · see citing works
Beyond collective intelligence: Collective adaptation
Galešić, M., Barkoczi, D., Berdahl, A. M., Biro, D., Carbone, G., Giannoccaro, I., Goldstone, R. L., González, C., Kandler, A., Kao, A. B., et al. (2023) · Journal of The Royal Society Interface · doi:10.1098/rsif.2022.0736 · Cited by 111 · see citing works
“A third important consideration is homophily , the phenomenon that people are typically surrounded by similar others [192] because of self-selection, mutual influence and/or common circumstances [193].”
A simple generative model of collective online behavior
Gleeson, J., Cellai, D., Onnela, J., Porter, M., & Reed-Tsochas, F. (2013) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.1313895111 · Cited by 101 · see citing works
“Given the difficulty in distinguishing between potential explanations of observed behavior at the individual level (6), it is useful to examine population-level models and attempt to reproduce empirically observed popularity distributions using the simplest possible assumptions about individual behavior.”
The Role of Social Influence in Security Feature Adoption
Das, S., Kramer, A. D. I., Dabbish, L., & Hong, J. I. (2015) · Conference on Computer Supported Cooperative Work · doi:10.1145/2675133.2675225 · Cited by 100 · see citing works
“There may be some forms of latent homophily [26] for which we did not control—for example, a user’s technical proficiency—that might explain some of the variance in our data.”
BackgroundZhang, J., Tong, L., Lamberson, P. J., Durazo-Arvizu, R. A., Luke, A., & Shoham, D. A. (2015) · Social Science & Medicine · doi:10.1016/j.socscimed.2014.05.049 · Cited by 97 · see citing works
Karsai, M., Íñiguez, G., Kikas, R., Kaski, K., & Kertész, J. (2016) · Scientific Reports · doi:10.1038/srep27178 · Cited by 95 · see citing works
“Although these choices are personal, they are not independent but potentially driven by several processes such as social influence [1], homophily [2], and information arriving from external sources like news or mass media [3].”
BackgroundSocial Networks and Causal Inference
VanderWeele, T., & An, W.-H. (2013) · Handbooks of sociology and social research · doi:10.1007/978-94-007-6094-3_17 · Cited by 95 · see citing works
“Shalizi and Thomas (2011) show that in the presence of latent homophily, even if there is no unmeasured environmental confounding, the associations comparing ego-perceived friends and alter-perceived friends may differ in magnitude even when there is no social influence.”
BackgroundInfluentialSocial Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016) · doi:10.2139/ssrn.2886526 · Cited by 763 · see citing works
Identifying Social Influence in Networks Using Randomized Experiments
Aral, S., & Walker, D. (2011) · IEEE Intelligent Systems · doi:10.1109/mis.2011.89 · Cited by 94 · see citing works
Integrating social and cognitive aspects of belief dynamics: towards a unifying framework
Galesic, M., Olsson, H., Dalege, J., van der Does, T., & Stein, D. (2020) · Journal of the Royal Society Interface · doi:10.1098/rsif.2020.0857 · Cited by 92 · see citing works
“For example, homophily may constrain the spread of beliefs and behaviours such as smoking, obesity and even divorce [63–65], and the resulting beliefs can influence processes such as network updating that in turn influence homophily [66].”
Background
- Authors
- Shalizi, C. R., & Thomas, A. C.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Shalizi, Cosma Rohilla
- Venue
- Sociological Methods & Research
- Publisher
- SAGE Publications
- Published
- 2011-05
- Volume
- 40
- Issue
- 2
- Pages
- 211-239
- ISSN
- 0049-1241 (ISSN-L)
- 1552-8294
- PMC
- PMC3328971
- OpenAlex
- W2149084727
- Cited by
- 994
- References
- 63
- Citations by year
- 2026: 32 · 2025: 41 · 2024: 46 · 2023: 57 · 2022: 63 · 2021: 122 · 2020: 66 · 2019: 76 · 2018: 61 · 2017: 76 · 2016: 75 · 2015: 67 · 2014: 66 · 2013: 79 · 2012: 42
- FWCI
- 50.87
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Sociological Methods & Research (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS047493)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Keywords
- homophily, social contagion, social influence, confounding, social networks, causal inference, imitation model, observational studies
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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