# Evidence of Complex Contagion of Information in Social Media: An Experiment Using Twitter Bots

## TL;DR

This study uses Twitter bots in a controlled experiment to demonstrate that information spreads on social media via complex contagion, where multiple exposures increase the likelihood of adoption, rather than simple independent exposure effects.

## Contribution

The paper introduces a novel experimental approach with social bots on Twitter and Bayesian models to distinguish between simple and complex contagion dynamics in information diffusion.

## Key findings

- Complex contagion better explains information spread on Twitter.
- Controlled experiments provide clearer evidence than observational studies.
- Results can inform strategies against misinformation and improve marketing tactics.

## 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.