Investigating the Observability of Complex Contagion in Empirical Social Networks

AAAI Conferences

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.


Wang

AAAI Conferences

How to identify influential nodes is a central research topic in information diffusion analysis. Many existing methods rely on the assumption that the network structure is completely known by the model. However, in many applications, such a network is either unavailable or insufficient to explain the underlying information diffusion phenomena. To address this challenge, we develop a multi-task sparse linear influence model (MSLIM), which can simultaneously predict the volume for each contagion and automatically identify sets of the most influential nodes for different contagions. Our method is based on the linear influence model with two main advantages: 1) it does not require the network structure; 2) it can detect different sets of the most influential nodes for different contagions.



Why scary clowns are threatening people all around the world

New Scientist

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.