Tracing information flow on a global scale using Internet chain-letter data

TLDR

Information circulates worldwide, yet the mechanism by which a single piece of content spreads globally remains unclear. The study traces individual-level propagation of widely circulated Internet chain letters. A probabilistic model that incorporates network clustering and asynchronous response times is used to reconstruct the propagation trees. Chain letters spread in narrow, deep tree-like patterns over hundreds of steps, challenging the small‑world view and indicating a more complex information spread.

Abstract

Although information, news, and opinions continuously circulate in the worldwide social network, the actual mechanics of how any single piece of information spreads on a global scale have been a mystery. Here, we trace such information-spreading processes at a person-by-person level using methods to reconstruct the propagation of massively circulated Internet chain letters. We find that rather than fanning out widely, reaching many people in very few steps according to "small-world" principles, the progress of these chain letters proceeds in a narrow but very deep tree-like pattern, continuing for several hundred steps. This suggests a new and more complex picture for the spread of information through a social network. We describe a probabilistic model based on network clustering and asynchronous response times that produces trees with this characteristic structure on social-network data.