The effects of network structure, competition and memory time on social spreading phenomena

TL;DR

This paper introduces an analytically tractable generative model for social spreading in online networks, highlighting how network structure and user memory influence meme popularity, matching empirical data characteristics.

Contribution

The paper presents a novel null model that separates the effects of network connectivity and user memory on information spread, advancing understanding of social media dynamics.

Findings

Model reproduces heavy-tailed meme popularity distributions

Distinguishes effects of network structure and memory time

Provides a null framework for social spreading analysis

Abstract

Online social media have greatly affected the way in which we communicate with each other. However, little is known about what are the fundamental mechanisms driving dynamical information flow in online social systems. Here, we introduce a generative model for online sharing behavior that is analytically tractable and which can reproduce several characteristics of empirical micro-blogging data on hashtag usage, such as (time-dependent) heavy-tailed distributions of meme popularity. The presented framework constitutes a null model for social spreading phenomena which, in contrast to purely empirical studies or simulation-based models, clearly distinguishes the roles of two distinct factors affecting meme popularity: the memory time of users and the connectivity structure of the social network.