Complex Contagions: A Decade in Review

Abstract

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.

Characterising the structure of the largest online commercial sex network in the UK: observational study with implications for STI prevention

“…For example, in a highly dense network or a network where individuals have high contact rates, diseases spread quickly as most of the members are closely connected to each other (Doherty et al 2005). STI transmission is also accelerated by relatively short paths between any two individuals, and the tendency toward clustering, or the tendency of an individual's contacts to have contacts among each other and to cluster into densely connected groups (Guilbeault, Becker, and Centola 2018). The presence of many unconnected pairs instead affects the incidence of STIs as it limits the extent of their diffusion (Doherty et al 2005).…”

“…This would require an understanding of how the diffusion of safe-sex practices-which is better understood as complex contagion, i.e. contagion that requires multiple contacts and social reinforcement (Guilbeault, Becker, and Centola 2018)-can be effectively achieved in online communities of sex workers and their clients, and how multiple contagions (e.g. STI diffusion and STI prevention campaigns) would interact in the network.…”

Phase transitions in information spreading on structured populations

“…These advances are particularly evident in the area of infectious disease forecasting where current models now incorporate realistic mobility and interaction data of human populations [1,2,3,4,5,6]. Analogously, social contagion phenomena that were initially modeled using the same mathematical framework as epidemics [7,8,9,10] are now described by complex contagion models [11,12,13] aimed at specifically characterizing processes such as the establishment of shared social norms and beliefs [14,15,16], the diffusion of knowledge and information [17,18], and the emergence of political consensus [19]. These models consider complex factors such as reinforcement and threshold mechanisms [20,21,22,23] and the loss of interest mediated by social interactions [24,25,26].…”

“…These models consider complex factors such as reinforcement and threshold mechanisms [20,21,22,23] and the loss of interest mediated by social interactions [24,25,26]. Furthermore, many of these theoretical approaches have put networks at the center of our understanding of social contagion phenomena and the information spreading process [27,28,8,17,29,30,31,32,12,33]. However, most theoretical and numerical work on the dynamics of social contagion focuses on highly stylized models, trading off the realistic features of human interactions for analytical transparency and computational efficiency.…”

Disentangling Sources of Influence in Online Social Networks

“…These are popular for their simplicity that facilitates theoretical analysis 23 , statistical inference from data 24 , and can also be used as building blocks for more complex applications such as influence maximization 25 . However, there are several crucial differences between epidemic spreading and information diffusion 26 . Epidemic spreading is better modeled with simple contagion model where endogenous factors play a dominant role, and the activation probabilities are independent of the neighborhood structure and the state of activated users in it.…”