# The Simple Rules of Social Contagion

arXiv: https://arxiv.org/abs/1308.5015 · HTML: https://mlsanity.com/paper/1308.5015/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2013.

## Built on by (most cited first, 9 of 9)

- [LLM-Based Social Simulations Require a Boundary](https://mlsanity.com/paper/2506.19806/) (2025)
- [Scalable Continuous-time Diffusion Framework for Network Inference and Influence Estimation](https://mlsanity.com/paper/2403.02867/) (2024)
- [Information Interaction Profile of Choice Adoption](https://mlsanity.com/paper/2104.13695/) (2021)
- [R-ODE: Ricci Curvature Tells When You Will be Informed](https://mlsanity.com/paper/2405.17282/) (2024)
- [Social-Inverse: Inverse Decision-making of Social Contagion Management with Task Migrations](https://mlsanity.com/paper/2209.10493/) (2022)
- [The Digital Ecosystem of Beliefs: does evolution favour AI over humans?](https://mlsanity.com/paper/2412.14500/) (2024)
- [Independent Asymmetric Embedding for Information Diffusion Prediction on Social Networks](https://mlsanity.com/paper/2105.08291/) (2021)
- [Improving Information Cascade Modeling by Social Topology and Dual Role User Dependency](https://mlsanity.com/paper/2204.08529/) (2022)
- [Uncovering Social Network Activity Using Joint User and Topic Interaction](https://mlsanity.com/paper/2506.12842/) (2025)

## Abstract (arXiv)

It is commonly believed that information spreads between individuals like a pathogen, with each exposure by an informed friend potentially resulting in a naive individual becoming infected. However, empirical studies of social media suggest that individual response to repeated exposure to information is significantly more complex than the prediction of the pathogen model. As a proxy for intervention experiments, we compare user responses to multiple exposures on two different social media sites, Twitter and Digg. We show that the position of the exposing messages on the user-interface strongly affects social contagion. Accounting for this visibility significantly simplifies the dynamics of social contagion. The likelihood an individual will spread information increases monotonically with exposure, while explicit feedback about how many friends have previously spread it increases the likelihood of a response. We apply our model to real-time forecasting of user behavior.