Social Contagions Research Articles

Under the influenceRevenge of the Tipping Point: Overstories, Superspreaders, and the Rise of Social Engineering Malcolm Gladwell Little, Brown, 2024. 368 pp.

A journalist revisits his popular theory of social contagions.

Paradoxes in the coevolution of contagions and institutions.

Epidemic models study the spread of undesired agents through populations, be it infectious diseases through a country, misinformation in social media or pests infesting a region. In combating these epidemics, we rely neither on global top-down interventions, nor solely on individual adaptations. Instead, interventions commonly come from local institutions such as public health departments, moderation teams on social media platforms or other forms of group governance. Classic models, which are often individual or agent-based, are ill-suited to capture local adaptations. We leverage developments of institutional dynamics based on cultural group selection to study how groups attempt local control of an epidemic by taking inspiration from the successes and failures of other groups. Incorporating institutional changes into epidemic dynamics reveals paradoxes: a higher transmission rate can result in smaller outbreaks as does decreasing the speed of institutional adaptation. When groups perceive a contagion as more worrisome, they can invest in improved policies and, if they maintain these policies long enough to have impact, lead to a reduction in endemicity. By looking at the interplay between the speed of institutions and the transmission rate of the contagions, we find rich coevolutionary dynamics that reflect the complexity of known biological and social contagions.

Social contagions in business resilience: Evidence from the U.S. restaurant industry in the COVID-19 pandemic

The unprecedented COVID-19 has led to the collapse of numerous businesses, notably within the tourism and hospitality sectors. Despite the burgeoning research on resilience, few studies have embraced a theoretical lens, particularly from a social network perspective. In addition, most extant resilience studies have not explicitly considered the geographic accessibility prerequisite inherent to tourism and hospitality products. In this study, leveraging the social contagion theory, we present a holistic research framework to investigate the influence of geographic and social proximities, two pivotal social contagion mechanisms, on business resilience. We also delve into moderating factors to discern the conditions under which contagion effects are amplified or attenuated. To validate our theoretical model, we select the restaurant industry as our research context, given its severe impact from COVID-19. Utilizing an extensive dataset from Yelp, encompassing ten U.S. cities varying in sizes and geolocations, our findings indicate that both geographic and social influences exert significant direct effects on resilience. Additionally, these effects exhibit considerable variations contingent upon product attributes, customer characteristics, and geographic factors. Theoretically, we are the first to substantiate the role of social contagion theory in examining resilience, enriching our understanding of the social network mechanism of behavioral contagion among customers during the COVID-19 pandemic. We also offer valuable practical implications for various stakeholders in supporting their management strategies and decision-making in developing effective plans and preparations, minimizing adverse impacts, and ensuring sustainability in the face of future disruptions.

Higher-order non-Markovian social contagions in simplicial complexes

Higher-order structures such as simplicial complexes are ubiquitous in numerous real-world networks. Empirical evidence reveals that interactions among nodes occur not only through edges but also through higher-dimensional simplicial structures such as triangles. Nevertheless, classic models such as the threshold model fail to capture group interactions within these higher-order structures. In this paper, we propose a higher-order non-Markovian social contagion model, considering both higher-order interactions and the non-Markovian characteristics of real-world spreading processes. We develop a mean-field theory to describe its evolutionary dynamics. Simulation results reveal that the theory is capable of predicting the steady state of the model. Our theoretical analyses indicate that there is an equivalence between the higher-order non-Markovian and the higher-order Markovian social contagions. Besides, we find that non-Markovian recovery can boost the system resilience to withstand a large-scale infection or a small-scale infection under different conditions. This work deepens our understanding of the behaviors of higher-order non-Markovian social contagions in the real world.

Long ties across networks accelerate the spread of social contagions.

Long ties across networks accelerate the spread of social contagions.

Enhancing predictive accuracy in social contagion dynamics via directed hypergraph structures

Evidence from both theoretical and empirical studies suggests that higher-order networks have emerged as powerful tools for modeling social contagions, such as opinion formation. In this article, we develop a model of social contagion on directed hypergraphs by considering the heterogeneity of individuals and environments in terms of reinforcing contagion effects. By distinguishing the directedness between nodes and hyperedges, we find that the bistable interval of the discontinuous phase transition decreases as the directedness strength decreases. Additionally, directed hypergraphs tend to generate bistable intervals when nodes with a large hyperdegree are more likely to adopt a specific opinion, as evidenced by simulations of directionality assignments for three sets of real networks. These findings provide two approaches to enhance the accuracy of predicting social contagion dynamics: one is to increase the stubbornness of all individuals, and the other is to prioritize increasing the stubbornness of highly influential individuals.

Social contagions with information sensitivity in complex networks

Social contagions with information sensitivity in complex networks

Pathways to discontinuous transitions in interacting contagion dynamics

Yet often neglected, dynamical interdependencies between concomitant contagion processes can alter their intrinsic equilibria and bifurcations. A particular case of interest for disease control is the emergence of discontinuous transitions in epidemic dynamics coming from their interactions with other simultaneous processes. To address this problem, here we propose a framework coupling a standard epidemic dynamics with another contagion process, presenting a tunable parameter shaping the nature of its transitions. Our model retrieves well-known results in the literature, such as the existence of first-order transitions arising from the mutual cooperation of epidemics or the onset of abrupt transitions when social contagions unidirectionally drive epidemics. We also reveal that negative feedback loops between simultaneous dynamical processes might suppress abrupt phenomena, thus increasing systems robustness against external perturbations. Our results render a general perspective toward finding different pathways to abrupt phenomena from the interaction of contagion processes.

The effects of individual heterogeneity on social contagions

Abstract Despite having significant effects on social contagions, individual heterogeneity has frequently been overlooked in earlier studies. To better understand the complexity of social contagions, a non-Markovian model incorporating heterogeneous social influence and adoption thresholds is introduced. For theoretical analysis, a generalized edge-based compartmental theory which considers the heterogeneities of social influence and adoption thresholds are developed. Focusing on the final adoption size, critical propagation probability and the phase transition type, social contagions for adoption thresholds follow normal distributions with various standard deviations, follow various distributions, and correlate with degrees are investigated. When thresholds follow normal distributions, a larger standard deviation results in a larger the final adoption size when the information propagation probability is relatively low. However, when the information propagation probability is relatively high, a larger standard deviation result in a smaller final adoption size. When thresholds follow various distributions, crossover phenomena in phase transition are observed in investigating the relationship of the final adoption size versus the average adoption threshold for some threshold distributions. When thresholds are correlated with degrees, similar crossover phenomena occur in investigating the relationship of the final adoption size versus the degree correlation index. Additionally, we find that increasing the heterogeneity of social influence suppresses the effects of adoption threshold heterogeneity on social contagions in the three cases. Our theory predictions agree well with the simulation results.

Overcoming vaccine hesitancy by multiplex social network targeting: an analysis of targeting algorithms and implications

Incorporating social factors into disease prevention and control efforts is an important undertaking of behavioral epidemiology. The interplay between disease transmission and human health behaviors, such as vaccine uptake, results in complex dynamics of biological and social contagions. Maximizing intervention adoptions via network-based targeting algorithms by harnessing the power of social contagion for behavior and attitude changes largely remains a challenge. Here we address this issue by considering a multiplex network setting. Individuals are situated on two layers of networks: the disease transmission network layer and the peer influence network layer. The disease spreads through direct close contacts while vaccine views and uptake behaviors spread interpersonally within a potentially virtual network. The results of our comprehensive simulations show that network-based targeting with pro-vaccine supporters as initial seeds significantly influences vaccine adoption rates and reduces the extent of an epidemic outbreak. Network targeting interventions are much more effective by selecting individuals with a central position in the opinion network as compared to those grouped in a community or connected professionally. Our findings provide insight into network-based interventions to increase vaccine confidence and demand during an ongoing epidemic.

How Does Network Structure Impact Socially Reinforced Diffusion?

How does network structure impact the speed and reach of social contagions? The current view holds that random links facilitate “simple” contagion, but when agents require multiple reinforcements for “complex” adoption, clustered networks are better conduits of social influence. We show that in complex contagion, even low probabilities of adoption upon a single contact would activate an exponential contagion process that tilts the balance in favor of random networks. On the other hand, underappreciated but critical to the race between random and clustered networks is how long agents engage with contagion. Switching back to prior practice and the inactivation of senders and especially receivers shorten the window of engagement for convincing distant contacts and weaken the reach of diffusion on random networks. We propose a simplified framework where clustering primarily enables contagion when repetition matters and receivers lose interest quickly; otherwise, diffusion, simple or complex, is faster on random networks than clustered ones. These mechanisms can inform designing social networks, structuring groups, and seeding of ideas and innovations at a time when the increasing inflow of content from various media limits actors’ engagement with each item, whereas expanding network size and connections speeds up diffusion through distant contacts. Supplemental Material: The e-companion is available at https://doi.org/10.1287/orsc.2023.1658 .

Automated discovery of analytical models for epidemic dynamics on coevolving networks

Coevolving network systems provide a framework for modeling the diffusion of social and biological contagions on networks, where network structure is allowed to coevolve with the contagion dynamics, describing phenomena such as the spread of epidemics on social networks where individuals rewire their connectivity to avoid infection. Along with direct computational simulations, coevolving network systems are often formulated in terms of systems of ordinary differential equations in their descriptive statistics. The differential equation approach reduces the computational burden of analyzing the systems, but deriving equations becomes difficult as the underlying model becomes more complicated and the desire for accuracy increases. We present a sparse model identification approach that learns the nonlinear differential equations for coevolving network systems using data from computational simulations. Using this approach, we construct a data-driven system of equations for a coevolving SIS model that reproduces system behavior in both temporal evolution and dependence of steady states on system parameters. Furthermore, we also present the development of a numerical differentiation scheme that performs better for our application than the standard ones in use.

The impacts of geographic and social influences on review helpfulness perceptions: A social contagion perspective

Identifying and presenting helpful reviews to customers can significantly affect their purchase decisions. Although review helpfulness has been extensively explored in tourism research, extant studies have not sufficiently emphasized the unique characteristics of tourism products and investigated review helpfulness perceptions from both geographic and social influence perspectives. In this study, drawing on social contagion theory, we developed a theoretical framework to examine the impact of social contagion, specifically geographic and social proximities, on perceived review helpfulness. Our empirical analyses of Yelp restaurant reviews indicated that geographic and social influences have varying impacts on review helpfulness perceptions. Additionally, social contagions significantly moderated the impacts of various review- and reviewer-related factors, and product characteristics further moderated the contagion effect on perceived review helpfulness. This study provides valuable theoretical and methodological contributions to research on review helpfulness, especially in tourism contexts, and lays out the practical implications for various stakeholders.

Simplicial cascades are orchestrated by the multidimensional geometry of neuronal complexes

Cascades over networks (e.g., neuronal avalanches, social contagions, and system failures) often involve higher-order dependencies, yet theory development has largely focused on pairwise-interaction models. Here, we develop a ‘simplicial threshold model’ (STM) for cascades over simplicial complexes that encode dyadic, triadic and higher-order interactions. Focusing on small-world models containing both short- and long-range k-simplices, we explore spatio-temporal patterns that manifest as a frustration between local and nonlocal propagations. We show that higher-order interactions and nonlinear thresholding coordinate to robustly guide cascades along a k-dimensional generalization of paths that we call ‘geometrical channels’. We also find this coordination to enhance the diversity and efficiency of cascades over a simplicial-complex model for a neuronal network, or ‘neuronal complex’. We support these findings with bifurcation theory and data-driven approaches based on latent geometry. Our findings provide fruitful directions for uncovering the multiscale, multidimensional mechanisms that orchestrate the spatio-temporal patterns of nonlinear cascades.

Effect of Contact Preference among Heterogeneous Individuals on Social Contagions

In social networks, individual heterogeneity is widely existed, and an individual often tends to contact more frequently with friends of similar status or opinion. It is worth noting that the contact preference characteristic among heterogeneous individuals will have a significant effect on social contagions. Thus, we propose a social contagion model which takes the heterogeneity of individual influence and contact preference into account, and make a theoretical analysis of the social spreading process by developing an edge-based compartmental theory. We find that the competition between simple contagion and complex contagion leads to the emergence of crossover phase transition phenomena when the influence of ordinary individuals is low: it changes from being hybrid to continuous, then to hybrid, and eventually to continuous phase transition with the increase of the contact probability of homogeneous individuals. However, it changes from being first-order to continuous phase transition when the influence strength of ordinary individuals is relatively high. In addition, there is an optimal value or range of contact probability of homogeneous individuals which maximizes the spreading size, and the optimal value or lower bound of the optimal range decreases with the increase of transmission probability. Our theoretical prediction results are in good agreement with the simulation results.

Effects of heterogeneous adoption thresholds on contact-limited social contagions

Limited contact capacity and heterogeneous adoption thresholds have been proven to be two essential characteristics of individuals in natural complex social systems, and their impacts on social contagions exhibit complex nature. With this in mind, a heterogeneous contact-limited threshold model is proposed, which adopts one of four threshold distributions, namely Gaussian distribution, log-normal distribution, exponential distribution and power-law distribution. The heterogeneous edge-based compartmental theory is developed for theoretical analysis, and the calculation methods of the final adoption size and outbreak threshold are given theoretically. Many numerical simulations are performed on the Erdös–Rényi and scale-free networks to study the impact of different forms of the threshold distribution on hierarchical spreading process, the final adoption size, the outbreak threshold and the phase transition in contact-limited propagation networks. We find that the spreading process of social contagions is divided into three distinct stages. Moreover, different threshold distributions cause different spreading processes, especially for some threshold distributions, there is a change from a discontinuous first-order phase transition to a continuous second-order phase transition. Further, we find that changing the standard deviation of different threshold distributions will cause the final adoption size and outbreak threshold to change, and finally tend to be stable with the increase of standard deviation.

Message passing approach for social contagions based on the trust probability with multiple influence factors

In social contagions, an individual to trust the behavioral information transmitted by neighbors depends on the level of the social status of neighbors as well as the closeness degree with neighbors. From the view of network topology, we propose the trust probability with multiple influence factors: node degree and the number of common neighbors. Furthermore, a weight factor is set to adjust the influence extent of the above two factors. As a result, in the context of the trust probability, we investigate social contagions with focus on social reinforcement and memory effect on networks, which are modeled by the threshold model. The message passing approach is adopted so as to formulate the state evolution of each node on the basis of network topology. Through extensive numerical simulations, we find that the trust probability can suppress social contagions, so does increasing the trust probability gap. Notably, the number of common neighbors as an influence factor of the trust probability is able to increase the final behavior adoption size, while node degree takes the opposite effect. The theoretical results are confirmed to agree well with the numerical results by the Monte Carlo method.

Investigating the Observability of Complex Contagion in Empirical Social Networks

Social contagion is the mechanism by which ideas and behaviors spread across human social networks. Simple contagion models approximate the likelihood of adoption as constant with each exposure to an "infected" network neighbor. However, social theory postulates that when adopting an idea or behavior carries personal or social risk, an individual's adoption likelihood also depends on the number of distinct neighbors who have adopted. Such complex contagions are thought to govern the spread of social movements and other important social phenomena. Online sites, such as Twitter, expose social interactions at a large scale and provide an opportunity to observe the spread of social contagions "in the wild." Much of the effort in searching for complex phenomena in real world contagions focuses on measuring user adoption thresholds. In this work, we show an alternative method for fitting probabilistic complex contagion models to empirical data that avoids measuring thresholds directly, and our results indicate bias in observed thresholds under both complex and simple models. We also show 1) that probabilistic models of simple and complex contagion are distinguishable when applied to an empirical social network with random user activity; and 2) the predictive power of these probabilistic adoption models against observed adoptions of actual hashtags used on Twitter. We use a set of tweets collected from Nigeria in 2014, focusing on 20 popular hashtags, using the follow graphs of the users adopting the tags during their initial peaks of activity.

A Generic Encapsulation to Unravel Social Spreading of a Pandemic: An Underlying Architecture

Cases of a new emergent infectious disease caused by mutations in the coronavirus family, called “COVID-19,” have spiked recently, affecting millions of people, and this has been classified as a global pandemic due to the wide spread of the virus. Epidemiologically, humans are the targeted hosts of COVID-19, whereby indirect/direct transmission pathways are mitigated by social/spatial distancing. People naturally exist in dynamically cascading networks of social/spatial interactions. Their rational actions and interactions have huge uncertainties in regard to common social contagions with rapid network proliferations on a daily basis. Different parameters play big roles in minimizing such uncertainties by shaping the understanding of such contagions to include cultures, beliefs, norms, values, ethics, etc. Thus, this work is directed toward investigating and predicting the viral spread of the current wave of COVID-19 based on human socio-behavioral analyses in various community settings with unknown structural patterns. We examine the spreading and social contagions in unstructured networks by proposing a model that should be able to (1) reorganize and synthesize infected clusters of any networked agents, (2) clarify any noteworthy members of the population through a series of analyses of their behavioral and cognitive capabilities, (3) predict where the direction is heading with any possible outcomes, and (4) propose applicable intervention tactics that can be helpful in creating strategies to mitigate the spread. Such properties are essential in managing the rate of spread of viral infections. Furthermore, a novel spectra-based methodology that leverages configuration models as a reference network is proposed to quantify spreading in a given candidate network. We derive mathematical formulations to demonstrate the viral spread in the network structures.

Impact of Immunization Strategies on the Dynamics of Social Contagions

Immunization strategies on complex networks are effective methods to control the spreading dynamics on complex networks, which change the topology and connectivity of the underlying network, thereby affecting the dynamics process of propagation. Here, we use a non-Markovian threshold model to study the impact of immunization strategies on social contagions, in which the immune index greater than (or equal to) 0 corresponds to targeted (random) immunization, and when the immune index is less than 0, the probability of an individual being immunized is inversely related to the degree of the individual. A generalized edge-based compartmental theory is developed to analyze the dynamics of social contagions under immunization, and theoretical predictions are very consistent with simulation results. We find that increasing the immune index or increasing the immune ratio will reduce the final adoption size and increase the outbreak threshold, in other words, make the residual network after immunization not conducive to social contagions. Interestingly, enhancing the network heterogeneity is proved to help improve the immune efficiency of targeted immunization. Besides, the dependence of the outbreak threshold on the network heterogeneity is correlated with the immune ratio and immune index.