Revisiting Information Diffusion Beyond Explicit Social Ties: A Study of Implicit-Link Diffusion on Twitter

TL;DR

This study investigates information diffusion on Twitter beyond explicit follow ties by distinguishing explicit versus implicit propagation paths. It introduces diffusion-graphbased metrics such as $RCI$, $IAR$, $SAR$, and $RER$ across four large datasets (Higgs, Nepal, Turkish, Ordinary) to quantify how implicit diffusion operates, grows cascades, and crosses communities. Key findings show that implicit diffusion is more likely at greater distances from the source, contributes less to overall cascade size than explicit diffusion, but more frequently traverses community boundaries; user susceptibility and inducement exhibit weak-to-moderate homophily and monophily, indicating socially embedded diffusion patterns. The work highlights the need to incorporate implicit-link diffusion into diffusion models and suggests directions for richer modeling of non-social exposure to improve predictions, interventions, and cross-platform understanding of information spread.

Abstract

Information diffusion on social media platforms is often assumed to occur primarily through explicit social connections, such as follower or friend ties. However, information frequently propagates beyond these observable ties -- through external websites, search engines, or algorithmic recommendations -- creating implicit links. How the presence of implicit links affects the diffusion process remains unclear. In this study, we investigate the characteristics of implicit links on Twitter using four large-scale datasets. Our analysis reveals that users who are farther from the original source in the social network are more likely to engage in diffusion via implicit links. Although implicit links contribute less to the overall diffusion volume than explicit links, they play a distinct role in disseminating content across diverse and topologically distant communities. We further examine the user attributes associated with the formation of implicit links and show that these features are unevenly distributed across the network and exhibit moderate levels of homophily and monophily. Together, these findings demonstrate that implicit links exert a meaningful influence on information diffusion and highlight the importance of incorporating them into models of diffusion and social influence.

Figures (15)

• Figure 1: Example of a follow network and cascade graph: (a) An illustration of a follow network where each directed link represents a follow relationship. Here, $B \to D$ indicates that $D$ is a follower of $B$. (b) An example of a cascade graph when a post by node D is reposted by nodes A, E, and F. Given that nodes E and F follow node D (as depicted in Fig. \ref{['figure:following_graph']}), the post is considered to be disseminated from node D to nodes E and F, thus resulting in the cascade graph having links $(D, E)$ and $(D, F)$. Similarly, since node A follows node E, the post is considered to be disseminated from node E to A, and the cascade graph has a link $(E, A)$.
• Figure 2: Example of a follow network and a cascade graph demonstrating nontrivial information diffusion via an implicit link: (a) An illustration of a follow network where each directed link represents a follow relationship. Here, $A \to C$ indicates that $C$ is a follower of $A$. (b) An example of a cascade graph where a post by node D is reposted by nodes A, C, E, and F. While E and F follow D, A does not. The pathway by which the information reached A is not traceable through explicit links and is thus considered to be implicit. Since C follows A, it can be inferred that C received the information from A through an explicit link.
• Figure 3: Correlation between implicit and explicit repost counts for each original post
• Figure 4: The proportion of reposting users among users located at a certain distance from the source
• Figure 5: The proportion of reposts via implicit links by distance from the source