Fink, Clay (The Johns Hopkins University) | Schmidt, Aurora C. (The Johns Hopkins University) | Barash, Vladimir (Graphika, Inc.) | Kelly, John (Graphika, Inc.) | Cameron, Christopher (Cornell University) | Macy, Michael (Cornell University)
Social contagion is the mechanism by which ideas and behaviors spread across human social networks. Simple contagion models approximate the likelihood of adoption as constant with each exposure to an "infected" network neighbor. However, social theory postulates that when adopting an idea or behavior carries personal or social risk, an individual's adoption likelihood also depends on the number of distinct neighbors who have adopted. Such complex contagions are thought to govern the spread of social movements and other important social phenomena. Online sites, such as Twitter, expose social interactions at a large scale and provide an opportunity to observe the spread of social contagions "in the wild." Much of the effort in searching for complex phenomena in real world contagions focuses on measuring user adoption thresholds. In this work, we show an alternative method for fitting probabilistic complex contagion models to empirical data that avoids measuring thresholds directly, and our results indicate bias in observed thresholds under both complex and simple models. We also show 1) that probabilistic models of simple and complex contagion are distinguishable when applied to an empirical social network with random user activity; and 2) the predictive power of these probabilistic adoption models against observed adoptions of actual hashtags used on Twitter. We use a set of tweets collected from Nigeria in 2014, focusing on 20 popular hashtags, using the follow graphs of the users adopting the tags during their initial peaks of activity.
It all started at a small housing complex in Greenville, South Carolina, with a group of children reporting a sinister clown in white face paint trying to lure them into the woods. Two months later, creepy clown sightings have spread to 37 US states, Canada and the UK. Just yesterday, there were reports of a knife-wielding clown apparently keeping commuters from leaving the New York City subway. The "killer clown" craze is spreading like a disease, says Nicholas Christakis at Yale University, who researches social contagion. This kind of mass hysteria has always been with us.
The advent and popularity of online social media also allows the creation of massive data sets which can inform models and underlying sociological theory. The ubiquity of smart devices (such as smart phones) also provides opportunities to gather extensive data on the behaviors and interactions of humans in real space. The goal of this symposium is to bring together a community of researchers interested in addressing these issues and to encourage interdisciplinary approaches to these problems.
With the emergence of computational social science as a field of collaboration between computer scientists and social scientists, the study of social networks and processes on these networks (social contagion) has been gaining interest. Many topics of traditional sociological interest (such as the diffusion of innovation, emergence of norms, identification of influencer) can now be studied using detailed computational models and extensive simulation. e advent and popularity of online social media also allows the creation of massive data sets which can inform models and underlying sociological theory. e ubiquity of smart devices (such as smart phones) also provides opportunities to gather extensive data on the behaviors and interactions of humans in real space.
Barrett, Christopher (Network Dynamics and Sim Science Lab) | Bisset, Keith (Network Dynamics and Sim Science Lab) | Leidig, Jonathan (Network Dynamics and Sim Science Lab) | Marathe, Achla (Network Dynamics and Sim Science Lab) | Marathe, Madhav V. (Network Dynamics and Sim Science Lab)
We discuss an interaction-based approach to study the coevolution between socio-technical networks, individual behaviors, and contagion processes on these networks. Finally, models of individual behaviors are composed with disease progression models to develop a realistic representation of the complex system in which individual behaviors and the social network adapt to the contagion. These methods are embodied within Simdemics – a general purpose modeling environment to support pandemic planning and response. New advances in network science, machine learning, high performance computing, data mining and behavioral modeling were necessary to develop Simdemics.