Social contagion theory: examining dynamic social networks and human behavior

By Nicholas A. Christakis, James H. Fowler. Statistics in Medicine, 2011

1,230 citations recorded by the source corpus.

Here, we review the research we have conducted on social contagion. We describe the methods we have employed (and the assumptions they have entailed) to examine several datasets with complementary strengths and weaknesses, including the Framingham Heart Study, the National Longitudinal Study of Adolescent Health, and other observational and experimental datasets that we and others have collected. We describe the regularities that led us to propose that human social networks may exhibit a ‘three degrees of influence’ property, and we review statistical approaches we have used to characterize interpersonal influence with respect to phenomena as diverse as obesity, smoking, cooperation, and happiness. We do not claim that this work is the final word, but we do believe that it provides some novel, informative, and stimulating evidence regarding social contagion in longitudinally followed networks. Along with other scholars, we are working to develop new methods for identifying causal effects using social network data, and we believe that this area is ripe for statistical development as current methods have known and often unavoidable limitations. Copyright © 2012 John Wiley & Sons, Ltd.

Subfields: Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/e0ccded71a98abeb8840d44e30ef71b5edf8969d

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Citations in the corpus

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Predicting traffic volumes and estimating the effects of shocks in massive transportation systems (2015, neutral): “+f ′ φDIST ×φOUT′, [8]”
Vital nodes identification in complex networks (2016, background): “However, inspired by the famous social contagious phenomena—“Three degrees of influence (which states that any individual’s social influence ceases beyond three degrees [269, 270]), and by mapping the spreading dynamics (SIR family) onto bond percolation [243] on complex networks, Hu et al.”
Detecting Emotional Contagion in Massive Social Networks (2014, background): “pleasurable feelings in others [16], by rewarding the efforts of”
Handbook of Big Data (2016, background): “Random graph theory was first introduced by the mathematicians Erdos and Renyi (1959). A random graph is simply a random variable whose sample space is a collection of graphs.”
Emotional contagion in organizational life (2018, methodology): “For example, using sophisticated social network analysis of large datasets (such as the Framingham Heart Study), Christakis and Fowler (2013) found that depressed individuals not only sought others who shared depressive symptoms, but also influenced each other’s moods over protracted periods of cont”
Social Influences on Interest (2016, methodology): “Researchers have used this technique to document the “spread” of obesity; smoking; alcohol consumption; happiness; drug use; divorce; and tastes in music, books, and movies (Christakis & Fowler, 2013; Lewis, Kaufman, Gonzalez, Wimmer, & Christakis, 2008).”
Estimating peer effects in networks with peer encouragement designs (2016, background): “10 and 11) or as providing upper bounds, rather than point estimates, for peer effects (6).”
Social Networks and Causal Inference (2013, methodology): “…observational longitudinal data with repeated measures has been used to attempt to draw conclusions about social influence on a network, even when data from only a single social network is available (Christakis and Fowler 2007, 2008, 2013; Snijders 2001, 2005; Steglich et al. 2010).”