Identifying Influential Links for Event Propagation on Twitter: A Network of Networks Approach

TL;DR

This paper investigates how follower links influence event spread on Twitter, using a Network of Networks model to identify and remove influential links, significantly reducing event propagation.

Contribution

It introduces a novel NoN-based framework to identify key follower links affecting event propagation on Twitter.

Findings

Removing influential links reduces event spread significantly.

Between-network links are crucial despite their small proportion.

The proposed model effectively identifies impactful follower links.

Abstract

Patterns of event propagation in online social networks provide novel insights on the modeling and analysis of information dissemination over networks and physical systems. This paper studies the importance of follower links for event propagation on Twitter. Three recent event propagation traces are collected with the Twitter user language field being used to identify the Network of Networks (NoN) structure embedded in the Twitter follower networks. We first formulate event propagation on Twitter as an iterative state equation, and then propose an effective score function on follower links accounting for the containment of event propagation via link removals. Furthermore, we find that utilizing the NoN model can successfully identify influential follower links such that their removals lead to a remarkable reduction in event propagation on Twitter follower networks. Experimental results find that the between-network follower links, though only account for a small portion of the total follower links, are crucial to event propagation on Twitter.