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This paper develops the theory of the social contagion of violence by proposing a four-part analytical framework that focuses on: (1) contagious narratives and the accompanying behavioural script about the use of violence as a response to those narratives; (2) population susceptibility to these narratives, in particular the role of worldviews and the underlying emotional landscape; (3) mechanisms of transmission, including physical and online social networks, public displays of violence and participation in violence; and (4) the role of contagion entrepreneurs. It argues that a similar four-part approach can be used to identify and imagine possibilities of counter-contagion. The application of the theory is illustrated through examination of the recent epidemic of violence against individuals accused of practising sorcery in the Enga province of Papua New Guinea, a place where such violence is a very new phenomenon.

Lyons (2011) offered several critiques of the social network analyses of
Christakis and Fowler, including issues of confounding, model inconsistency,
and statistical dependence in networks. Here we show that in some settings,
social network analyses of the type employed by Christakis and Fowler will
still yield valid tests of the null of no social contagion, even though
estimates and confidence intervals may not be valid. In particular, we show
that if the alter's state is lagged by an additional period, then under the
null of no contagion, the problems of model inconsistency and statistical
dependence effectively disappear which allow for testing for contagion. Our
results clarify the setting in which even "flawed" social network analyses
are still useful for assessing social contagion and social
influence.

""Contents""; ""Abbreviations and acronyms""; ""Illustrations""; ""Preface and acknowledgements""; ""1 Merchants of death""; ""2 War by other means""; ""3 The evils of quarantine""; ""4 Quarantine and the empire of free trade""; ""5 Yellow fever resurgent""; ""6 A stranglehold on the East""; ""7 Plague and the global economy""; ""8 Protection or protectionism?""; ""9 Disease and globalization""; ""Conclusion Sanitary pasts, sanitary futures""; ""Notes""; ""Bibliography""; ""Index

New ideas, products, and practices take time to diffuse, a fact that is often attributed to some form of heterogeneity among potential adopters. This paper examines three broad classes of diffusion models—contagion, social influence, and social learning—and shows how to incorporate heterogeneity into each at a high level of generality without losing analytical tractability. Each type of model leaves a characteristic "footprint" on the shape of the adoption curve which provides a basis for discriminating empirically between them. The approach is illustrated using the classic study of Ryan and Gross (1943) on the diffusion of hybrid corn. (JEL D83, O33, Q16, Z13)

Social influence plays an important role in human behavior and decisions. Sources of influence can be divided as external, which are independent of social context, or as originating from peers, such as family and friends. An important question is how to disentangle the social contagion by peers from external influences. While a variety of experimental and observational studies provided insight into this problem, identifying the extent of contagion based on large-scale observational data with an unknown network structure remains largely unexplored. By bridging the gap between the large-scale complex systems perspective of collective human dynamics and the detailed approach of social sciences, we present a parsimonious model of social influence, and apply it to a central topic in political science—elections and voting behavior. We provide an analytical expression of the county vote-share distribution, which is in excellent agreement with almost a century of observed U.S. presidential election data. Analyzing the social influence topography over this period reveals an abrupt phase transition from low to high levels of social contagion, and robust differences among regions. These results suggest that social contagion effects are becoming more instrumental in shaping large-scale collective political behavior, with implications on democratic electoral processes and policies.

Risk perceptions have, to a great extent, been studied exclusively as individual cognitive mechanisms in which individuals collect, process, and form perceptions as atomized units unconnected to a social system. These individual‐level theories do not, however, help explain how perception of risk may vary between communities or within a single community. One alternative approach is based on a network theory of contagion. This approach, emerging largely from organizational and community social network studies, suggests that it is the relational aspects of individuals and the resulting networks and self‐organizing systems that influence individual perceptions and build "groups or communities of like‐minded" individuals. These social units, it is argued, behave as attitude, knowledge, or behavioral structures. The study reported in this article tests one aspect of this theoretical perspective. The central hypothesis proposes the existence of risk perception networks—relational groupings of individuals who share, and perhaps create, similar risk perceptions. To test this idea, data were collected from individuals involved in a community environmental conflict over a hazardous waste site cleanup. The statistical analysis used a matrix of relational social linkages to compare with a matrix of individual risk perceptions. The analysis confirmed the hypothesis suggesting that social linkages in communities may play an important role in focusing risk perceptions.

AbstractThis study aimed to support the theory of popularity contagion, which posits that popularity spreads among friends spontaneously and regardless of behavioral changes. Peer nominations of status and behavior were collected annually between 6th and 12th grades from a total of 1062 adolescents. Longitudinal hypotheses were mostly supported using path analyses, showing (1) that individual popularity could be predicted by friends' popularity levels over time, even when controlling for stability of individual popularity; (2) that this prediction was not accounted for by behavioral contagion of aggressive or prosocial behaviors; and (3) that individual social preference generally could not be predicted by friends' preference levels over time. Implications, limitations, and directions for future research are discussed.