Abstract
The current research observed historical public discourse surrounding the issue of open science given its contemporary salience across scientific disciplines. The Twitter API was used to collect the population of 1,723,169 open science-related tweets published from Twitter’s inception through September 2022. A latent factor Dirichlet multinomial mixture (LF-DMM) model was used to analyze textual tweet content. Stepwise segmented compositional regression identified evolutionary trends and revolutionary shifts in the discourse over time. Seven themes emerged, and results demonstrated nuanced trajectories in the representation and prevalence of themes occurring in public discourse about open science over time.
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