Low-Vocality Engagement Shapes Online Participation

2026-06-19

AI Summary

This study addresses the overemphasis in existing research on high-visibility online participation—such as posting—while overlooking prevalent yet low-visibility behaviors like liking and following. Leveraging over three billion user interaction logs from the Bluesky platform, the authors propose a two-dimensional analytical framework centered on “engagement intensity” and “expression style.” Through large-scale behavioral modeling, state-transition identification, and higher-order motivational inference, they demonstrate that low-visibility actions play a pivotal role in sustaining user engagement. Key findings reveal that high-intensity engagement is predominantly manifested through liking rather than posting, whereas low-intensity users are more likely to post; inactive states serve as selective re-entry thresholds; and low-intensity liking reliably predicts subsequent high engagement. These results challenge the posting-centric paradigm by showing how low-visibility practices structurally shape platform participation ecosystems.

Abstract

Online participation is often measured through visible expression, especially posting, yet many consequential forms of engagement occur through less vocal actions such as liking and following. Here we study how users inhabit Bluesky by reconstructing participation profiles from more than three billion activity records produced by a near-complete sample accounting for more than 80\% of registered users. We aggregate behavior into monthly user-level observations and distinguish two dimensions that are often conflated in platform analytics: intensity, capturing how much users engage, and style, capturing how engagement is expressed across actions. We find that vocal production is highly concentrated, but low-posting behavior does not imply absence from platform participation. High-intensity engagement is most strongly associated with liking rather than posting, while posting-oriented participation is more common among low-intensity users, indicating that visibility and sustained engagement should not be conflated. Transition patterns suggest that high-intensity likers and posters could be described as attractors; network-building redirects users within the active space; whereas observed inactivity acts as a persistent boundary that selectively limits re-entry. Higher-order motifs further show that inactivity often interrupts rather than erases prior regimes, and that low-intensity liking can precede durable high-intensity engagement. These results show that online participation is structured by differentiated low-vocality practices, calling for a shift from post-centered measures of activity toward dynamic accounts of platform presence. We identify a broader challenge for computational social science: platform participation cannot be adequately understood through the behavior of vocal minorities alone.

Problem

• online participation
• low-vocality engagement
• platform analytics
• user behavior
• computational social science

Innovation

• low-vocality engagement
• participation intensity
• participation style
• behavioral motifs
• platform presence