Damon Centola

The spread of behavior in an online social network experiment.

Join the Club An important question for policy-makers is how to communicate information (for example, about public health interventions) and promote behavior change most effectively across a population. The structure of a social network can dramatically affect the diffusion of behavior through a population. Centola (p. 1194) examined whether the number of individuals choosing to register for a health forum could be influenced by an artificially constructed network of neighbors that were signed up for the forum. The behavior spread more readily on clustered networks than on random, poorly clustered ones. Certain types of behavior within human systems are thus more likely to spread if people are exposed to many other people who have already adopted the behavior (for example, in the circumstances where your friends know each other, as well as yourself). An online experiment shows how network structure affects the spread of health behavior. How do social networks affect the spread of behavior? A popular hypothesis states that networks with many clustered ties and a high degree of separation will be less effective for behavioral diffusion than networks in which locally redundant ties are rewired to provide shortcuts across the social space. A competing hypothesis argues that when behaviors require social reinforcement, a network with more clustering may be more advantageous, even if the network as a whole has a larger diameter. I investigated the effects of network structure on diffusion by studying the spread of health behavior through artificially structured online communities. Individual adoption was much more likely when participants received social reinforcement from multiple neighbors in the social network. The behavior spread farther and faster across clustered-lattice networks than across corresponding random networks.

Complex Contagions and the Weakness of Long Ties

The strength of weak ties is that they tend to be long—they connect socially distant locations, allowing information to diffuse rapidly. The authors test whether this “strength of weak ties” generalizes from simple to complex contagions. Complex contagions require social affirmation from multiple sources. Examples include the spread of high‐risk social movements, avant garde fashions, and unproven technologies. Results show that as adoption thresholds increase, long ties can impede diffusion. Complex contagions depend primarily on the width of the bridges across a network, not just their length. Wide bridges are a characteristic feature of many spatial networks, which may account in part for the widely observed tendency for social movements to diffuse spatially.

An experimental study of homophily in the adoption of health behavior.

Greater similarity of paired obese participants in an online network increased access to and adoption of health behaviors. How does the composition of a population affect the adoption of health behaviors and innovations? Homophily—similarity of social contacts—can increase dyadic-level influence, but it can also force less healthy individuals to interact primarily with one another, thereby excluding them from interactions with healthier, more influential, early adopters. As a result, an important network-level effect of homophily is that the people who are most in need of a health innovation may be among the least likely to adopt it. Despite the importance of this thesis, confounding factors in observational data have made it difficult to test empirically. We report results from a controlled experimental study on the spread of a health innovation through fixed social networks in which the level of homophily was independently varied. We found that homophily significantly increased overall adoption of a new health behavior, especially among those most in need of it.

Homophily, Cultural Drift and the Co-Evolution of Cultural Groups

Studies of cultural differentiation have shown that social mechanisms that normally lead to cultural convergence—homophily and influence—can also explain how distinct cultural groups can form. However, this emergent cultural diversity has proven to be unstable in the face of cultural drift—small errors or innovations that allow cultures to change from within. The authors develop a model of cultural differentiation that combines the traditional mechanisms of homophily and influence with a third mechanism of network homophily, in which network structure co-evolves with cultural interaction. Results show that in certain regions of the parameter space, these co-evolutionary dynamics can lead to patterns of cultural diversity that are stable in the presence of cultural drift. The authors address the implications of these findings for understanding the stability of cultural diversity in the face of increasing technological trends toward globalization.

The Emperor's dilemma : A computational model of self-enforcing norms

The authors demonstrate the uses of agent‐based computational models in an application to a social enigma they call the “emperor’s dilemma,” based on the Hans Christian Andersen fable. In this model, agents must decide whether to comply with and enforce a norm that is supported by a few fanatics and opposed by the vast majority. They find that cascades of self‐reinforcing support for a highly unpopular norm cannot occur in a fully connected social network. However, if agents’ horizons are limited to immediate neighbors, highly unpopular norms can emerge locally and then spread. One might expect these cascades to be more likely as the number of “true believers” increases, and bridge ties are created between otherwise distant actors. Surprisingly, the authors observed quite the opposite effects.

Cascade dynamics of complex propagation

Random links between otherwise distant nodes can greatly facilitate the propagation of disease or information, provided contagion can be transmitted by a single active node. However, we show that when the propagation requires simultaneous exposure to multiple sources of activation, called complex propagation, the effect of random links can be just the opposite; it can make the propagation more difficult to achieve. We numerically calculate critical points for a threshold model using several classes of complex networks, including an empirical social network. We also provide an estimation of the critical values in terms of vulnerable nodes.

The spontaneous emergence of conventions: An experimental study of cultural evolution

Significance Social conventions shape every aspect of our lives, from how we greet each other to the languages we speak. Yet their origins have been a topic of theoretical speculation since the time of Aristotle. Most approaches assume that institutions are necessary to organize large populations, but the simplest explanation is that universally accepted conventions are the unintended consequence of individuals’ efforts to coordinate locally with one another. Although this hypothesis is compelling, it lacks conclusive empirical support. Here, we present results from controlled experiments demonstrating that changes in network connectivity can cause global social conventions to spontaneously emerge from local interactions, even though people have no knowledge about the population, or that they are coordinating at a global scale. How do shared conventions emerge in complex decentralized social systems? This question engages fields as diverse as linguistics, sociology, and cognitive science. Previous empirical attempts to solve this puzzle all presuppose that formal or informal institutions, such as incentives for global agreement, coordinated leadership, or aggregated information about the population, are needed to facilitate a solution. Evolutionary theories of social conventions, by contrast, hypothesize that such institutions are not necessary in order for social conventions to form. However, empirical tests of this hypothesis have been hindered by the difficulties of evaluating the real-time creation of new collective behaviors in large decentralized populations. Here, we present experimental results—replicated at several scales—that demonstrate the spontaneous creation of universally adopted social conventions and show how simple changes in a population’s network structure can direct the dynamics of norm formation, driving human populations with no ambition for large scale coordination to rapidly evolve shared social conventions.

Social Media and the Science of Health Behavior

Social influences are a primary factor in the adoption of health behaviors.1,2 Compliance with diet and nutrition programs, adherence to preventive screening recommendations, and maintenance of exercise routines all can depend on having contact with friends and family who also engage in these behaviors. In addition to a great deal of literature on peer effects,3 recent studies of large network data sets have made important advances in our understanding of how social networks influence the collective dynamics of health behavior.4,5 Research has shown that social influences can affect collective health outcomes ranging from epidemic obesity to smoking behaviors, which have important consequences both for theoretical models of social epidemiology and for the practical design of interventions and treatment strategies.6,7 These findings have direct implications for research aimed at understanding how social influences on dieting, exercising, medication use, and getting screenings can impact behavior change affecting cardiovascular disease. The large number of health domains affected by recent research on the spread of behaviors has made social diffusion a topic of growing interest for an increasing variety of researchers and practitioners who are concerned with understanding the social dimensions of health. This article discusses the development of new methods that use social media to study these health dynamics. Although there is widespread theoretical and practical interest in understanding how social influences affect health-related behaviors, empirical studies of the social dynamics of health face important methodological challenges. Large observational studies of population health have faced the limitation that they are unable to address problems of causal identification.8,9 Extant studies have been able to show conclusively that health-related traits such as smoking5 and weight gain4 correlate with social ties in a network, yet the data do not provide a …

Homophily, networks, and critical mass: Solving the start-up problem in large group collective action

Formal theories of collective action face the problem that in large groups a single actor makes such a small impact on the collective good that cooperation is irrational. Critical mass theorists argue that this ‘large group problem’ can be solved by an initial critical mass of contributors, whose efforts can produce a ‘bandwagon’ effect, making cooperation rational for the remaining members of the population. However, critical mass theory requires an explanation of how a critical mass can form in the first place. I present a model of collective action that solves this problem by showing how aspects of social structure – including network topology, homophily, and local coalition formation – can allow rational actors to endogenously form a critical mass. The findings indicate that as the mobilization effort becomes more ‘complex’, clustered networks and homophily become increasingly important for critical mass collective action.

The Social Origins of Networks and Diffusion.

Recent research on social contagion has demonstrated significant effects of network topology on the dynamics of diffusion. However, network topologies are not given a priori. Rather, they are patterns of relations that emerge from individual and structural features of society, such as population composition, group heterogeneity, homophily, and social consolidation. Following Blau and Schwartz, the author develops a model of social network formation that explores how social and structural constraints on tie formation generate emergent social topologies and then explores the effectiveness of these social networks for the dynamics of social diffusion. Results show that, at one extreme, high levels of consolidation can create highly balkanized communities with poor integration of shared norms and practices. As suggested by Blau and Schwartz, reducing consolidation creates more crosscutting circles and significantly improves the dynamics of social diffusion across the population. However, the author finds that further reducing consolidation creates highly intersecting social networks that fail to support the widespread diffusion of norms and practices, indicating that successful social diffusion can depend on moderate to high levels of structural consolidation.