Spreading dynamics of information on online social networks

TL;DR

This paper analyzes large-scale social media data to uncover a universal mechanism driving information spread, revealing how social reinforcement and weakening effects shape dissemination patterns and network clustering influences burst phenomena.

Contribution

It introduces a simple equation modeling information spreading dynamics across multiple platforms, unifying diverse empirical observations and resolving previous controversies.

Findings

Social reinforcement and weakening are key drivers of information spread.

The proposed model accurately describes empirical spreading trajectories.

Network clustering causes rapid, high-frequency information bursts.

Abstract

Social media is profoundly changing our society with its unprecedented spreading power. Due to the complexity of human behaviors and the diversity of massive messages, the information spreading dynamics are complicated, and the reported mechanisms are different and even controversial. Based on data from mainstream social media platforms, including WeChat, Weibo, and Twitter, cumulatively encompassing a total of 7.45 billion users, we uncover a ubiquitous mechanism that the information spreading dynamics are basically driven by the interplay of social reinforcement and social weakening effects. Accordingly, we propose a concise equation, which, surprisingly, can well describe all the empirical large-scale spreading trajectories. Our theory resolves a number of controversial claims and satisfactorily explains many phenomena previously observed. It also reveals that the highly clustered nature of social networks can lead to rapid and high-frequency information bursts with relatively small coverage per burst. This vital feature enables social media to have a high capacity and diversity for information dissemination, beneficial for its ecological development.