Comparing information diffusion mechanisms by matching on cascade size
Abstract
Do different types of information spread differently online? In recent years, studies have sought answers to such questions by comparing statistical properties of network paths taken by different kinds of content diffusing online. Here, we demonstrate the importance of controlling for correlations between properties being compared. In particular, we show that previously reported structural differences between diffusion paths of false and true news on Twitter disappear when comparing only cascades of the same size; differences between diffusion paths of images, videos, news, and petitions persist. Paired with a theoretical analysis of diffusion processes, our results suggest that, in order to limit the spread of false news, it may be enough to focus on reducing the mean “infectiousness” of the information.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 08, 2021
- Source ID
- 10.1073/pnas.2100786118
Entities
People
- Johan Ugander
- Jonas Lybker Juul
Organizations
- Army Research Office
- Cornell University
- Stanford University