Social Contagion Theory: Examining Dynamic Social Networks and Human Behavior

TL;DR

This paper reviews research on social contagion, exploring how behaviors and traits spread through social networks, highlighting the 'three degrees of influence' phenomenon and discussing statistical methods for causal inference.

Contribution

It introduces new insights into social contagion effects and proposes novel statistical approaches for analyzing influence in dynamic social networks.

Findings

Evidence of 'three degrees of influence' in social networks

Influence observed in obesity, smoking, cooperation, happiness

Development of new methods for causal inference in network data

Abstract

Here, we review the research we have done on social contagion. We describe the methods we have employed (and the assumptions they have entailed) in order 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 inter-personal 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.