Topological measures for identifying and predicting the spread of complex contagions
Nature Communications (2021)
Douglas Guilbeault, Damon Centola
The center of a network is the best place for seeding a new idea, and traditionally, social science has always placed the most highly connected individuals at the center. The existing models for identifying network influencers, however, assume that the idea that is spreading is a simple contagion in which people need exposure to only one other person in order to be influenced. While these work well for spreading simple ideas, they fall short for complex ones. When it comes to complex contagions for which people require exposure to multiple others in order to be influenced, these models fail to reach significant spread. In this study, we introduce “Complex Centrality,” the first and only model for effectively predicting the mass spread of complex contagions. Complex Centrality shifts the center of a network to network hotspots, or hidden social clusters in the outer edges of every network that have a detectable support infrastructure that enables individuals to initiate the spread of new ideas. Innovations seeded following our model regularly spread to over 70% of a population. Now, instead of looking for important people in a network, we look for important places.
Experimental evidence for scale-induced category convergence across populations
Nature Communications (2021)
Douglas Guilbeault, Andrea Baronchelli, Damon Centola
Everything around us is categorized. But how do shared categories emerge across independent societies and cultures? Sociologists have approached this question via two competing models. One holds that people share categories because they have an innate predisposition to represent the world’s structure in similar ways, while the second holds that people construct meaning dynamically in social networks and that this interaction can lead to very different systems across societies. Anthropological data indicates, however, that large, independent societies consistently arrive at highly similar category systems across different topics. In this study, we find that more variation in categories representing the same idea does arise when communication happens within small groups. Yet, when communication happens within large social networks, the lexical diversity among individuals converges into highly similar category systems and produces replicable society-level patterns. As people suggest ideas within social networks, certain ones are reinforced as they are repeated in people’s interactions with one another until one idea gets enough traction that it reaches a tipping point that leads the entire population to consensus.
Impact of network structure on collective learning: An experimental study in a data science competition
PLoS ONE (2020)
Devon Brackbill, Damon Centola
Do efficient communication networks increase innovation?  Scientists, engineers and strategists all work within highly connected environments where each person’s solutions are used to inspire and inform the work of others. The communication networks between researchers can determine the rate at which new ideas and innovations reach the rest of the community, giving rise to better solutions to difficult problems. As the complexity of the problem increases, so does the putative need for more efficient collaboration networks.  Firms, research organizations and universities have all invested in developing network technology (including the Internet itself) to improve communications between researchers trying to solve complex programs.   However, recent theoretical evidence suggests that these efforts may be counterproductive. These theories suggest that past a certain point of connectivity, increasing network efficiency can actually reduce the overall scientific progress of members of communication networks.  The Annenberg Data Science Competition is a year-long open competition for Data Scientists worldwide, designed to study how changes to the collaboration networks between researchers affect their ability to solve complex problems. Our goal is to understand whether less efficient collaboration networks do in fact increase collective intelligence.
The Complex Contagion of Doubt in the Anti-Vaccine Movement
Sabin-Aspen Vaccine Science & Policy Group Annual Report (2020)
Damon Centola
Sociologist Damon Centola (2019) describes anti-vaccine sentiment as a “complex contagion” that requires reinforcement by multiple social peers to reinforce its legitimacy. This process takes place in real and virtual communities when people who don’t understand an issue wait for a subset of their peers to respond to it. Once ideas and images that aren’t supported by the weight of evidence have become accepted parts of a “controversy,” it legitimizes the notion that vaccines may be harmful and creates a bias toward inaction. People in general feel a greater moral responsibility for any harm that comes about through something they have done than for a task they have neglected, and the hypothesized harm from vaccination may appear more immediate than the danger of the pathogens against which vaccines protect.
Networked collective intelligence improves dissemination of scientific information regarding smoking risks
PLoS ONE (2020)
Douglas Guilbeault, Damon Centola
Despite substantial investments in public health campaigns, misunderstanding of healthrelated scientific information is pervasive. This is especially true in the case of tobacco use, where smokers have been found to systematically misperceive scientific information about the negative health effects of smoking, in some cases leading smokers to increase their prosmoking bias. Here, we extend recent work on ‘networked collective intelligence’ by testing the hypothesis that allowing smokers and nonsmokers to collaboratively evaluate antismoking advertisements in online social networks can improve their ability to accurately assess the negative health effects of tobacco use. Using Amazon’s Mechanical Turk, we conducted an online experiment where smokers and nonsmokers (N = 1600) were exposed to anti-smoking advertisements and asked to estimate the negative health effects of tobacco use, either on their own or in the presence of peer influence in a social network. Contrary to popular predictions, we find that both smokers and nonsmokers were surprisingly inaccurate at interpreting anti-smoking messages, and their errors persisted if they continued to interpret these messages on their own. However, smokers and nonsmokers significantly improved in their ability to accurately interpret anti-smoking messages by sharing their opinions in structured online social networks. Specifically, subjects in social networks reduced the error of their risk estimates by over 10 times more than subjects who revised solely based on individual reflection (p < 0.001, 10 experimental trials in total). These results suggest that social media networks may be used to activate social learning that improves the public’s ability to accurately interpret vital public health information.
Physician networks and the complex contagion of clinical treatment
JAMA Network Open (2020)
Damon Centola
One of the greatest challenges in contemporary research on quality of care is to understand unexplained regional variation in physicians’ use of new medical treatments. Keating et al offer valuable new insight into this problem by studying physicians’ uptake of the biological cancer therapy bevacizumab. To identify the sources of variation, Keating et al1 developed a compelling new approach. Over the course of 4 years, starting in 2005 to 2006, they examined the prescription behavior of 829 oncologists across 432 practices and 405 distinct communities.
Perceptions of cervical cancer prevention on Twitter uncovered by different sampling strategies
PLoS ONE (2019)
Gem M. Le, Kate Radcliffe, Courtney Lyles, Helena C. Lyson, Byron Wallace, George Sawaya, Rena Pasick, Damon Centola, Urmimala Sarkar
We obtained publicly-available Twitter data from 2014 using three sampling strategies (topranked, simple random sample, and topic model) based on key words related to cervical cancer prevention. We conducted a content analysis of 100 tweets from each of the three samples and examined the extent to which the narratives and frequency of themes differed across samples.  Advocacy-related tweets constituted the most prevalent theme to emerge across all three sample types, and were most frequently found in the top-ranked sample. A random sample detected the same themes as topic modeling, but the relative frequency of themes identified from topic modeling fell in-between top-ranked and random samples. Variations in themes uncovered by different sampling methods suggest it is useful to qualitatively assess the relative frequency of themes to better understand the breadth and depth of social media conversations about health.
Facts or stories? How to use social media for cervical cancer prevention: A multi-method study of the effects of sender type and content type on increased message sharing
Preventive Medicine (2019)
Jingwen Zhang, Gem Le, David Larochelle, Rena Pasick, George F. Sawaya, Urmimala Sarkar, Damon Centola
We conducted a multi-method study to identify the effects of sender type (individuals or organizations) and content type (personal experiences or factual information) on promoting the spread of cervical cancer prevention messages over social media. First, we used observational Twitter data to examine correlations between sender type and content type with retweet activity. Then, we constructed 900 experimental tweets according to a 2 (sender type) by 2 (content type) factorial design and tested their probabilities of being shared in an online platform. Personal experience tweets and organizational senders were associated with more retweets. However, the experimental study revealed that informational tweets were shared significantly more than personal experience tweets; and organizational senders were shared significantly more than individual senders. While rare personal experience messages can achieve large success, they are generally unsuccessful; however, there is a reproducible causal effect of messages that use organizational senders and factual information for achieving greater peer-to-peer dissemination.
Social networks and health: New developments in diffusion, online and offline
Annual Review of Sociology (2019)
Jingwen Zhang, Damon Centola
The relationship between social networks and health encompasses everything from the flow of pathogens and information to the diffusion of beliefs and behaviors. This review addresses the vast and multidisciplinary literature that studies social networks as a structural determinant of health. In particular, we report on the current state of knowledge on how social contagion dynamics influence individual and collective health outcomes. We pay specific attention to research that leverages large-scale online data and social network experiments to empirically identify three broad classes of contagion processes: pathogenic diffusion, informational and belief diffusion, and behavioral diffusion. We conclude by identifying the need for more research on (a) how multiple contagions interact within the same social network, (b) how online social networks impact offline health, and (c) the effectiveness of social network interventions for improving population health.
Influential Networks
Nature Human Behaviour (2019)
Damon Centola
While simple contagions spread efficiently from highly connected ‘influencers’, new research has revealed another kind of spreading process, that of complex contagions, which follows surprisingly different pathways to disperse through social networks.
The Truth About Behavioral Change
MIT Sloan Management Review (2019)
Damon Centola
The latest thinking on social networks explains why new technologies and innovative behaviors really spread. It’s not about “going viral”.
The Wisdom of Partisan Crowds
Proceedings of the National Academy of Sciences (2019)
Joshua Becker, Ethan Porter, Damon Centola
Normative theories of deliberative democracy are based on the premise that social information processing can improve group beliefs. Research on the “wisdom of crowds” has found that information exchange can increase belief accuracy in many cases, but theories of political polarization imply that groups will become more extreme—and less accurate—when beliefs are motivated by partisan political bias. While this risk is not expected to emerge in politically heterogeneous networks, homogeneous social networks are expected to amplify partisan bias when people communicate only with members of their own political party. However, we find that the wisdom of crowds is robust to partisan bias. Social influence not only increases accuracy but decreases polarization without between-group network ties.
Social media as a tool to promote health awareness: Results from an online cervical cancer prevention study
Journal of Cancer Education (2018)
Helena Lyson, Gem Le, Jingwen Zhang, Natalie Rivadeneira, Courtney Lyles, Kate Radcliffe, Rena Pasick, George Sawaya, Urmimala Sarkar, Damon Centola
Online social media platforms represent a promising opportunity for public health promotion. Research is limited, however, on the effectiveness of social media at improving knowledge and awareness of health topics and motivating healthy behavior change. Therefore, we investigated whether participation in an online social media platform and receipt of brief, tailored messages is effective at increasing knowledge, awareness, and prevention behaviors related to human papillomavirus (HPV) and cervical cancer. Our findings suggest that most study participants had substantial knowledge, awareness, and engagement in positive behaviors related to cervical cancer prevention at the start of the study. Nevertheless, we found that HPV awareness can be increased through brief participation in an online social media platform and receipt of tailored health messages.
Social learning and partisan bias in the interpretation of climate trends
Proceedings of the National Academy of Sciences (2018)
Douglas Guilbeault, Joshua Becker, Damon Centola
Scientific communications about climate change are frequently misinterpreted due to motivated reasoning, which leads some people to misconstrue climate data in ways that conflict with the intended message of climate scientists. Attempts to reduce partisan bias through bipartisan communication networks have found that exposure to diverse political views can exacerbate bias. Here, we find that belief exchange in structured bipartisan networks can significantly improve the ability of both conservatives and liberals to interpret climate data, eliminating belief polarization. We also find that social learning can be reduced, and polarization maintained, when the salience of partisanship is increased, either through exposure to the logos of political parties or through exposure to political identity markers.
Experimental evidence for tipping points in social convention
Science (2018)
Damon Centola, Joshua Becker, Devon Brackbill, Andrea Baronchelli
Once a population has converged on a consensus, how can a group with a minority viewpoint overturn it? Theoretical models have emphasized tipping points, whereby a sufficiently large minority can change the societal norm. Groups of people who had achieved a consensus about the name of a person shown in a picture were individually exposed to a confederate who promoted a different name. The only incentive was to coordinate. When the number of confederates was roughly 25% of the group, the opinion of the majority could be tipped to that of the minority.
How social networks shape social comparison
Social Comparison in Judgment and Behavior (2018)
Jingwen Zhang, Damon Centola
While social comparison research has focused on the processes and consequences of how the comparer gleans information from the comparison other (individual or group), recent research on social networks demonstrates how information and influence is distributed across persons in a network. This chapter reviews social influence processes in social networks. We first review recent research on social comparison and its negative consequences in online social networks. Then we delve into discussing the social network causes of biased social perceptions online and how this can be remedied by building more accurate perceptions through constructed online networks. Lastly, we discuss findings from recent experimental studies that illustrate how constructed online networks can harness social comparison to induce significant changes in health behavior.
Complex Contagions: A Decade in Review
Springer Nature (2018)
Douglas Guilbeault, Joshua Becker, Damon Centola
Since the publication of ‘Complex Contagions and the Weakness of Long Ties’ in 2007, complex contagions have been studied across an enormous variety of social domains. In reviewing this decade of research, we discuss recent advancements in applied studies of complex contagions, particularly in the domains of health, innovation diffusion, social media, and politics. We also discuss how these empirical studies have spurred complementary advancements in the theoretical modeling of contagions, which concern the effects of network topology on diffusion, as well as the effects of individual-level attributes and thresholds. In synthesizing these developments, we suggest three main directions for future research. The first concerns the study of how multiple contagions interact within the same network and across networks, in what may be called an ecology of contagions. The second concerns the study of how the structure of thresholds and their behavioral consequences can vary by individual and social context. The third area concerns the roles of diversity and homophily in the dynamics of complex contagion, including both diversity of demographic profiles among local peers, and the broader notion of structural diversity within a network. Throughout this discussion, we make an effort to highlight the theoretical and empirical opportunities that lie ahead.
Network dynamics of social influence in the wisdom of crowds
Proceedings of the National Academy of Sciences (2017)
Joshua Becker, Devon Brackbill, Damon Centola
Medical decisions are based on scientific research which allows physicians to make treat patients based on probability estimates about the likelihood of diagnoses and treatment efficacy. Where medical diagnoses are made in conditions of uncertainty, physicians must rely on their judgement to generate the best decision possible. At the same time, large variations in medical procedure between geographical regions indicate that medical decisions are also subject to social influence by peers and community norms. Fortunately, research on the wisdom of crowds has shown that the average belief within a group is generally more accurate than any given individual. Our pilot tests on simple estimation tasks have shown that by allowing people to share information with each other, individuals can draw on the wisdom of crowds to improve the accuracy of their beliefs. This project is designed to understand how encouraging information flow between physicians and institutions can improve the accuracy of medical diagnoses.
Learning is robust to noise in decentralized networks.
Proceedings of the National Academy of Sciences (2017)
Joshua Becker, Devon Brackbill, Damon Centola
Our study presents a theoretical model and experimental test of how social influence affects the wisdom of crowds. Our theoretical simulations show that the accuracy of the group mean can improve in decentralized networks when the weight that individuals place on their own estimates is positively correlated with their accuracy. Empirically, our findings confirm this prediction, showing that a correlation between individual accuracy and self-weight can explain how social influence improves the mean of the group estimate.
Birth Control Connections: the Effect of Online Social Communication on Contraceptive Attitudes
Contraception (2017)
Edith Fox, Sijia Yang, Jingwen Zhang, Damon Centola, Christine Dehlendorf
Peers are valued sources of contraceptive information. It is not known whether this influence extends to virtual peers and therefore whether online peer-to-peer platforms could affect contraceptive attitudes, knowledge and behavior. To explore the impact of online social communication, we conducted a randomized trial on the effect on non-IUD users of web-based communication with IUD users.
Support or competition? How online social networks increase physical activity:
A randomized controlled trial

Preventive Medicine Reports (2016)
Jingwen Zhang, Devon Brackbill, Sijia Yang, Joshua Becker, Natalie Herbert, Damon Centola
Online social networks have become a highly attractive target for large scale health initiatives; however, there is insufficient knowledge about why online networks might be effective sources of social influence for improving physical activity levels. In a randomized controlled trial, we evaluate the effects of social support and social comparison independently, and in combination, to determine how social motivations for behavior change directly impact people’s exercise activity.
Identifying the effects of social media on health behavior: Data from a large-scale online experiment
Data in Brief (2015)
Jingwen Zhang, Devon Brackbill, Sijia Yang, and Damon Centola
Sedentary lifestyle is an escalating epidemic. Little is known about whether or how social media can be used to design a cost-effective solution for sedentary lifestyle. In this article we describe the data from a randomized controlled trial (RCT) that evaluated two prominent strategies for conducting exercise interventions using elements of social media: motivational media campaigns and online peer networks.
The social origins of networks and diffusion
American Journal of Sociology (2015)
Damon Centola
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.

  • Awarded 2017 James Coleman Award for Outstanding Article, Rationality and Society Section of the American Sociological Association
The spontaneous emergence of conventions: An experimental study of cultural evolution
Proceedings of the National Academy of Sciences (2015)
Damon Centola and Andrea Baronchelli
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.
Choosing your network: Social preferences in an online health community
Social Science and Medicine (2014)
Damon Centola and Arnout van der Rijt
A growing number of online health communities offer individuals the opportunity to receive information, advice, and support from peers. Recent studies have demonstrated that these new online contacts can be important informational resources, and can even exert significant influence on individuals’ behavior in various contexts.
Social media and the science of health behavior
Circulation (2013)
Damon Centola
The recent explosion of social media provides significant new opportunities for health researchers to study how social interactions affect the dynamics of behavior change.
A simple model of stability in critical mass dynamics
Journal of Statistical Physics (2013)
Damon Centola
While strong social incentives, such as peer-enforcement, can facilitate the growth of collective action and collective behavior, these incentives can also compromise the dynamics of long term stability.
The spread of behavior in an online social network experiment (Supporting materials)
Science (2010)
Damon Centola
Experimental results show that behaviors spread farther and faster through clustered-lattice networks than through ‘randomized’ networks.

  • Awarded 2011 Best Article in Mathematical Sociology,American Sociological Association
  • Awarded 2011 Goodwin Award for Outstanding Contribution to Sociological Methodology, American Sociological Association
Failure in complex social networks
Journal of Mathematical Sociology (2009)
Damon Centola
Scale-free networks can be far more vulnerable to failure due to random attacks than more homogeneously distributed exponential networks.
Complex contagions and the weakness of long ties
American Journal of Sociology (2007)
Damon Centola and Michael Macy
When behavioral adoption requires peer reinforcement, adding weak ties to a social network can actually slow down (and even prevent entirely) the diffusion process.

  • Awarded 2009 Best Article in Mathematical Sociology, American Sociological Association
Cascade dynamics of complex propagation
Physica A (2007)
Damon Centola, Victor Eguiluz, and Michael Macy
Randomizing permutations on ordered social networks can cause phase transitions in the collective dynamics of diffusion.
Homophily, cultural drift, and the co-evolution of cultural groups
Journal of Conflict Resolution (2007)
Damon Centola, Juan Carlos Avella, Victor Eguiluz, and Maxi San Miguel
Allowing networks to evolve endogenously provides a mechanism for understanding how the “homogenizing” forces of homophily and social influence can produce cultural diversity.
The emperor’s dilemma: A computational model of self-enforcing norms
American Journal of Sociology (2005)
Damon Centola, Robb Willer, and Michael Macy
We investigate how normative behaviors that can be detrimental to everyone in a population can nonetheless wind up not only spreading, but also being enforced by every member of the population.

  • Awarded 2006 Best Article in Mathematical Sociology, American Sociological Association
Social life in silico: The science of artificial societies
Handbook of Group Research and Practice (2005)
Damon Centola and Michael Macy
Computational modeling has become well established as an essential methodology in the biological and physical sciences, and has recently begun a migration into the social sciences. In physics, systems with non-linear dynamics and sensitive dependence on initial conditions (so-called “complex systems”) have motivated the use of a wide variety of computational techniques. Similarly, social scientists have begun to appreciate that the complexity of social systems cannot be understood using traditional analytical techniques.