Revisiting Information Diffusion Beyond Explicit Social Ties: A Study of Implicit-Link Diffusion on Twitter
This research addresses the problem of incomplete diffusion models for social media platforms like Twitter by highlighting the role of implicit links, though it is incremental as it builds on existing studies of social ties.
The study investigated how implicit links, such as those from external websites or algorithmic recommendations, affect information diffusion on Twitter, finding that users farther from the original source are more likely to use these links and that they help disseminate content across diverse communities.
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.