Social Contagion Processes Research Articles

Simplicial models of social contagion

Complex networks have been successfully used to describe the spread of diseases in populations of interacting individuals. Conversely, pairwise interactions are often not enough to characterize social contagion processes such as opinion formation or the adoption of novelties, where complex mechanisms of influence and reinforcement are at work. Here we introduce a higher-order model of social contagion in which a social system is represented by a simplicial complex and contagion can occur through interactions in groups of different sizes. Numerical simulations of the model on both empirical and synthetic simplicial complexes highlight the emergence of novel phenomena such as a discontinuous transition induced by higher-order interactions. We show analytically that the transition is discontinuous and that a bistable region appears where healthy and endemic states co-exist. Our results help explain why critical masses are required to initiate social changes and contribute to the understanding of higher-order interactions in complex systems.

Process of Social Contagion of Motivation between Teacher and Learners

Process of Social Contagion of Motivation between Teacher and Learners

Social contagion model induced by the effect of distinct social contexts

Almost all existing mathematical models of social contagion have been based on the assumption that the contagion probability is controlled by the number of exposures exerted by affected neighbors. However, recent empirical studies in several social contagion processes have revealed that the contagion probability is tightly controlled by the number of distinct social contexts formed by affected neighbors, rather than the actual number of exposures. Inspired by these empirical results, we propose a social contagion model with susceptible–adopted–susceptible dynamics in multi-relational networks. We incorporate the effect of distinct social contexts into our model by assuming that the contagion probability is determined by the number of layers to which an individual is exposed. Our model exhibits a rich variety of phase transitions containing continuous, discontinuous, and double transitions. We identify three universal classes of collective dynamics in the model and determine the conditions for the transitions between these classes. The bifurcation analysis shows that the transition conditions are dependent on the transmission probabilities of other layers and the probabilities of being adopted by one-layer and two-layer exposures, respectively. The theoretical results derived from Microscopic Markov-Chain Approach and mean-field theory are in good agreement with numerical simulations.

Influencers identification in complex networks through reaction-diffusion dynamics

A pivotal idea in network science, marketing research and innovation diffusion theories is that a small group of nodes -- called influencers -- have the largest impact on social contagion and epidemic processes in networks. Despite the long-standing interest in the influencers identification problem in socio-economic and biological networks, there is not yet agreement on which is the best identification strategy. State-of-the-art strategies are typically based either on heuristic centrality metrics or on analytic arguments that only hold for specific network topologies or peculiar dynamical regimes. Here, we leverage the recently introduced random-walk effective distance -- a topological metric that estimates almost perfectly the arrival time of diffusive spreading processes on networks -- to introduce a new centrality metric which quantifies how close a node is to the other nodes. We show that the new centrality metric significantly outperforms state-of-the-art metrics in detecting the influencers for global contagion processes. Our findings reveal the essential role of the network effective distance for the influencers identification and lead us closer to the optimal solution of the problem.

A review of the use of optimal control in social models

Optimal control is a powerful optimization technique derived from the mathematical theory of the Calculus of Variations. It can be employed to maximize the returns from and minimize the costs associated with physical, social, and economic processes. In recent years, optimal control theory has been utilized to develop ideal intervention strategies for a variety of “contagious” social ailments that are spread chiefly by contact with affected peers—like crime, substance abuse and the rampant infiltration of internet worms and viruses in computerized systems. As the dynamics of these processes are akin to that of an epidemic, the compartmental models utilized for studying the spread of infectious diseases can be easily adapted for these types of problems. In this article, we review the use of optimal control theory in the design of cost effective intervention strategies for the successful mitigation of social contagion processes.

Interactive social contagions and co-infections on complex networks.

What we are learning about the ubiquitous interactions among multiple social contagion processes on complex networks challenges existing theoretical methods. We propose an interactive social behavior spreading model, in which two behaviors sequentially spread on a complex network, one following the other. Adopting the first behavior has either a synergistic or an inhibiting effect on the spread of the second behavior. We find that the inhibiting effect of the first behavior can cause the continuous phase transition of the second behavior spreading to become discontinuous. This discontinuous phase transition of the second behavior can also become a continuous one when the effect of adopting the first behavior becomes synergistic. This synergy allows the second behavior to be more easily adopted and enlarges the co-existence region of both behaviors. We establish an edge-based compartmental method, and our theoretical predictions match well with the simulation results. Our findings provide helpful insights into better understanding the spread of interactive social behavior in human society.

Modeling information diffusion in time-varying community networks.

Social networks are rarely static, and they typically have time-varying network topologies. A great number of studies have modeled temporal networks and explored social contagion processes within these models; however, few of these studies have considered community structure variations. In this paper, we present a study of how the time-varying property of a modular structure influences the information dissemination. First, we propose a continuous-time Markov model of information diffusion where two parameters, mobility rate and community attractiveness, are introduced to address the time-varying nature of the community structure. The basic reproduction number is derived, and the accuracy of this model is evaluated by comparing the simulation and theoretical results. Furthermore, numerical results illustrate that generally both the mobility rate and community attractiveness significantly promote the information diffusion process, especially in the initial outbreak stage. Moreover, the strength of this promotion effect is much stronger when the modularity is higher. Counterintuitively, it is found that when all communities have the same attractiveness, social mobility no longer accelerates the diffusion process. In addition, we show that the local spreading in the advantage group has been greatly enhanced due to the agglomeration effect caused by the social mobility and community attractiveness difference, which thus increases the global spreading.

Reach and speed of judgment propagation in the laboratory

In recent years, a large body of research has demonstrated that judgments and behaviors can propagate from person to person. Phenomena as diverse as political mobilization, health practices, altruism, and emotional states exhibit similar dynamics of social contagion. The precise mechanisms of judgment propagation are not well understood, however, because it is difficult to control for confounding factors such as homophily or dynamic network structures. We introduce an experimental design that renders possible the stringent study of judgment propagation. In this design, experimental chains of individuals can revise their initial judgment in a visual perception task after observing a predecessor's judgment. The positioning of a very good performer at the top of a chain created a performance gap, which triggered waves of judgment propagation down the chain. We evaluated the dynamics of judgment propagation experimentally. Despite strong social influence within pairs of individuals, the reach of judgment propagation across a chain rarely exceeded a social distance of three to four degrees of separation. Furthermore, computer simulations showed that the speed of judgment propagation decayed exponentially with the social distance from the source. We show that information distortion and the overweighting of other people's errors are two individual-level mechanisms hindering judgment propagation at the scale of the chain. Our results contribute to the understanding of social-contagion processes, and our experimental method offers numerous new opportunities to study judgment propagation in the laboratory.

Modeling Contagion Through Social Networks to Explain and Predict Gunshot Violence in Chicago, 2006 to 2014

Every day in the United States, more than 200 people are murdered or assaulted with a firearm. Little research has considered the role of interpersonal ties in the pathways through which gun violence spreads. To evaluate the extent to which the people who will become subjects of gun violence can be predicted by modeling gun violence as an epidemic that is transmitted between individuals through social interactions. This study was an epidemiological analysis of a social network of individuals who were arrested during an 8-year period in Chicago, Illinois, with connections between people who were arrested together for the same offense. Modeling of the spread of gunshot violence over the network was assessed using a probabilistic contagion model that assumed individuals were subject to risks associated with being arrested together, in addition to demographic factors, such as age, sex, and neighborhood residence. Participants represented a network of 138 163 individuals who were arrested between January 1, 2006, and March 31, 2014 (29.9% of all individuals arrested in Chicago during this period), 9773 of whom were subjects of gun violence. Individuals were on average 27 years old at the midpoint of the study, predominantly male (82.0%) and black (75.6%), and often members of a gang (26.2%). Explanation and prediction of becoming a subject of gun violence (fatal or nonfatal) using epidemic models based on person-to-person transmission through a social network. Social contagion accounted for 63.1% of the 11 123 gunshot violence episodes; subjects of gun violence were shot on average 125 days after their infector (the person most responsible for exposing the subject to gunshot violence). Some subjects of gun violence were shot more than once. Models based on both social contagion and demographics performed best; when determining the 1.0% of people (n = 1382) considered at highest risk to be shot each day, the combined model identified 728 subjects of gun violence (6.5%) compared with 475 subjects of gun violence (4.3%) for the demographics model (53.3% increase) and 589 subjects of gun violence (5.3%) for the social contagion model (23.6% increase). Gunshot violence follows an epidemic-like process of social contagion that is transmitted through networks of people by social interactions. Violence prevention efforts that account for social contagion, in addition to demographics, have the potential to prevent more shootings than efforts that focus on only demographics.

A social contagious model of the obesity epidemic

Obesity has been recognized as a global epidemic by WHO, followed by many empirical evidences to prove its infectiousness. However, the inter-person spreading dynamics of obesity are seldom studied. A distinguishing feature of the obesity epidemic is that it is driven by a social contagion process which cannot be perfectly described by the infectious disease models. In this paper, we propose a novel belief decision model based on the famous Dempster-Shafer theory of evidence to model obesity epidemic as the competing spread of two obesity-related behaviors: physical inactivity and physical activity. The transition of health states is described by an SIS model. Results reveal the existence of obesity epidemic threshold, above which obesity is quickly eradicated. When increasing the fading level of information spread, enlarging the clustering of initial obese seeds, or introducing small-world characteristics into the network topology, the threshold is easily met. Social discrimination against the obese people plays completely different roles in two cases: on one hand, when obesity cannot be eradicated, social discrimination can reduce the number of obese people; on the other hand, when obesity is eradicable, social discrimination may instead cause it breaking out.

When behaviour turns contagious: the use of deterministic epidemiological models in modeling social contagion phenomena

Mathematical models offer crucial insights into the transmission dynamics and control of infectious diseases. These models have also been applied to investigate a variety of ‘contagious’ social phenomena like crime, opinions, addiction and fanaticism. We review the use and adaptation of models from epidemiology (compartmental models) to investigate the transmission dynamics of different social contagion processes- all of which are spread by contact only.

Statistical fluctuations in pedestrian evacuation times and the effect of social contagion.

Mathematical models of pedestrian evacuation and the associated simulation software have become essential tools for the assessment of the safety of public facilities and buildings. While a variety of models is now available, their calibration and test against empirical data are generally restricted to global averaged quantities; the statistics compiled from the time series of individual escapes ("microscopic" statistics) measured in recent experiments are thus overlooked. In the same spirit, much research has primarily focused on the average global evacuation time, whereas the whole distribution of evacuation times over some set of realizations should matter. In the present paper we propose and discuss the validity of a simple relation between this distribution and the microscopic statistics, which is theoretically valid in the absence of correlations. To this purpose, we develop a minimal cellular automaton, with features that afford a semiquantitative reproduction of the experimental microscopic statistics. We then introduce a process of social contagion of impatient behavior in the model and show that the simple relation under test may dramatically fail at high contagion strengths, the latter being responsible for the emergence of strong correlations in the system. We conclude with comments on the potential practical relevance for safety science of calculations based on microscopic statistics.

Emotions and Activity Profiles of Influential Users in Product Reviews Communities

Viral marketing seeks to maximize the spread of a campaign through an online social network, often targeting influential nodes with high centrality. In this article, we analyze behavioral aspects of influential users in trust-based product reviews communities, quantifying emotional expression, helpfulness, and user activity level. We focus on two independent product review communities, Dooyoo and Epinions, in which users can write product reviews and define trust links to filter product recommendations. Following the patterns of social contagion processes, we measure user social influence by means of the k-shell decomposition of trust networks. For each of these users, we apply sentiment analysis to extract their extent of positive, negative, and neutral emotional expression. In addition, we quantify the level of feedback they received in their reviews, the length of their contributions, and their level of activity over their lifetime in the community. We find that users of both communities exhibit a large heterogeneity of social influence, and that helpfulness votes and age are significantly better predictors of the influence of an individual than sentiment. The most active of the analyzed communities shows a particular structure, in which the inner core of users is qualitatively different from its periphery in terms of a stronger positive and negative emotional expression. These results suggest that both objective and subjective aspects of reviews are relevant to the communication of subjective experience.

Stylized Facts in Brazilian Vote Distributions.

Elections, specially in countries such as Brazil, with an electorate of the order of 100 million people, yield large-scale data-sets embodying valuable information on the dynamics through which individuals influence each other and make choices. In this work we perform an extensive analysis of data sets available for Brazilian proportional elections of legislators and city councilors throughout the period 1970–2014, which embraces two distinct political regimes: a military regime followed by a democratic one. We perform a comparative analysis of elections for legislative positions, in different states and years, through the distribution p(v) of the number of candidates receiving v votes. We show the impact of the different political regimes on the vote distributions. Although p(v) has a common shape, with a scaling behavior, quantitative details change over time and from one electorate to another. In order to interpret the observed features, we propose a multi-species model consisting in a system of nonlinear differential equations, with values of the parameters that reflect the heterogeneity of candidates. In its simplest setting, the model can not explain the cutoff, formed by the most voted candidates, whose success is determined mainly by their peculiar, intrinsic characteristics, such as previous publicity. However, the modeling allows to interpret the scaling of p(v), yielding a predictor of the degree of feedback in the interactions of the electorate. Knowledge of the feedback is relevant beyond the context of elections, since a similar interactivity may occur for other social contagion processes in the same population.

Epidemic processes in complex networks

In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and sociotechnical systems. The complex properties of real-world networks have a profound impact on the behavior of equilibrium and nonequilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, mathematicians, epidemiologists, computer, and social scientists share a common interest in studying epidemic spreading and rely on similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while focusing on the main results and the paradigmatic models in infectious disease modeling, the major results concerning generalized social contagion processes are also presented. Finally, the research activity at the forefront in the study of epidemic spreading in coevolving, coupled, and time-varying networks is reported.

Abrupt transitions from reinfections in social contagions

The study of social contagion processes is of utmost importance for understanding the emergence of collective social states. Here we introduce reinfections in the Susceptible-Infected-Recovered (SIR) model so to incorporate the possibility that an individual that ceases its activity (recovered) can resume it due to secondary infections from its active (infected) peers. We show that, when primary infection is less frequent than secondary ones, a typical situation in many social contagion processes, the epidemic transition turns from smooth to abrupt. As a consequence, macroscopic collective states can be triggered from the inactive (healthy) regime by a small increment of the primary contagion rate.

Modelling the propagation of social response during a disease outbreak

Epidemic trajectories and associated social responses vary widely between populations, with severe reactions sometimes observed. When confronted with fatal or novel pathogens, people exhibit a variety of behaviours from anxiety to hoarding of medical supplies, overwhelming medical infrastructure and rioting. We developed a coupled network approach to understanding and predicting social response. We couple the disease spread and panic spread processes and model them through local interactions between agents. The social contagion process depends on the prevalence of the disease, its perceived risk and a global media signal. We verify the model by analysing the spread of disease and social response during the 2009 H1N1 outbreak in Mexico City and 2003 severe acute respiratory syndrome and 2009 H1N1 outbreaks in Hong Kong, accurately predicting population-level behaviour. This kind of empirically validated model is critical to exploring strategies for public health intervention, increasing our ability to anticipate the response to infectious disease outbreaks.

Top-K structural diversity search in large networks

Social contagion depicts a process of information (e.g., fads, opinions, news) diffusion in the online social networks. A recent study reports that in a social contagion process, the probability of contagion is tightly controlled by the number of connected components in an individual’s neighborhood. Such a number is termed structural diversity of an individual, and it is shown to be a key predictor in the social contagion process. Based on this, a fundamental issue in a social network is to find top-\(k\) users with the highest structural diversities. In this paper, we, for the first time, study the top-\(k\) structural diversity search problem in a large network. Specifically, we study two types of structural diversity measures, namely, component-based structural diversity measure and core-based structural diversity measure. For component-based structural diversity, we develop an effective upper bound of structural diversity for pruning the search space. The upper bound can be incrementally refined in the search process. Based on such upper bound, we propose an efficient framework for top-\(k\) structural diversity search. To further speed up the structural diversity evaluation in the search process, several carefully devised search strategies are proposed. We also design efficient techniques to handle frequent updates in dynamic networks and maintain the top-\(k\) results. We further show how the techniques proposed in component-based structural diversity measure can be extended to handle the core-based structural diversity measure. Extensive experimental studies are conducted in real-world large networks and synthetic graphs, and the results demonstrate the efficiency and effectiveness of the proposed methods.

Is fertility contagious? Using panel data to disentangle mechanisms of social network influences on fertility decisions.

Using panel data (N = 1.679 married and cohabiting couples), this paper investigates the presence and causal mechanisms of social contagion processes regarding first births. Results confirmed the hypothesized positive association between the number of network members (friends, acquaintances, siblings) with young children and the respondents' transition rate into parenthood, particularly among younger couples. Several potential intervening mechanisms underlying this social contagion effect were tested. First, evidence was found for observational learning processes in which Ego obtained information on the joys and challenges of parenthood from network members with children. Second, childless respondents tended to feel pressured from couples with children in the network to start a family. Third, results supported the notion of social opportunity costs in that the anticipated loss of social ties after becoming a parent was more likely the fewer parents there were in the network. All three mechanisms exerted a positive impact on both fertility intentions and behavior. Panel regression models relying on intraindividual change scores showed that social learning was the most robust mechanism. An additional indirect test for causality suggested that the findings were unlikely to merely reflect parental status homophily (i.e., selection effects).

Social Contagion Process in Informal Warning Networks to Understand Evacuation Timing Behavior

Individual evacuation decisions are often characterized by the influence of one's social network, referred to as informal warning network. In this article, a threshold model of social contagion, originally introduced in the network science literature, is proposed to characterize this social influence in the evacuation decision-making process, in particular the timing of evacuation decision. Simulation models are developed to investigate the effects of community mixing patterns and the strength of ties on timing of evacuation decision.