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.
Highlights
The diffusion of information and ideas in complex social systems has fascinated the research community for decades [1]
We had the bots tweet at irregular intervals, but with frequencies set according to a circadian pattern
We propose two contagion models, namely simple contagion model (SC), in which all exposure attempts are considered to be independent, and complex contagion threshold model (CC), and derive quantitative predictions for them
Summary
The diffusion of information and ideas in complex social systems has fascinated the research community for decades [1]. The first proposal to use epidemiological models for the analysis of the spreading of ideas was put forth more than fifty years ago [2]. Such models, where each exposure results in the same adoption probability, are referred to as simple contagion models. It was subsequently suggested, that more complex effects might come into play when considering the spread of ideas rather than diseases.
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