Title: Evidence of Complex Contagion of Information in Social Media: An Experiment Using Twitter Bots,
Abstract: It has recently become possible to study the dynamics of information
diffusion in techno-social systems at scale, due to the emergence of online
platforms, such as Twitter, with millions of users. One question that
systematically recurs is whether information spreads according to simple or
complex dynamics: does each exposure to a piece of information have an
independent probability of a user adopting it (simple contagion), or does this
probability depend instead on the number of sources of exposure, increasing
above some threshold (complex contagion)? Most studies to date are
observational and, therefore, unable to disentangle the effects of confounding
factors such as social reinforcement, homophily, limited attention, or network
community structure. Here we describe a novel controlled experiment that we
performed on Twitter using `social bots' deployed to carry out coordinated
attempts at spreading information. We propose two Bayesian statistical models
describing simple and complex contagion dynamics, and test the competing
hypotheses. We provide experimental evidence that the complex contagion model
describes the observed information diffusion behavior more accurately than
simple contagion. Future applications of our results include more effective
defenses against malicious propaganda campaigns on social media, improved
marketing and advertisement strategies, and design of effective network
intervention techniques.