On measuring change in networked publics: a case study of United States election publics on Twitter from 2020 to 2022

Networked publics are the imagined communities that form on technologically-mediated spaces, often to deliberate on current events and enduring issues. Measuring change in networked publics is practically difficult because of the different ways that change in such communities can be operationalized. Yet measuring how networked publics are changing is particularly pressing, as researchers, platforms, and civil society have proposed interventions to curb some of their behaviors in the wake of digitally-mediated disinformation and incitement to violence. Here, I propose a tri-factor model for measuring change in publics centered on creators, audiences, and platforms, and demonstrate this model on a case study of United States ‘networked election publics’ on Twitter from 2020 to 2022. Analyzing over 600M posts from 2020 to 2022, I find that right-leaning creators and audiences decreased in their activity relative to left-leaning publics in 2022, and faced increased platform moderation via suspensions and shadowbanning. However, these negative signs are offset by a relative growth in the size of right-leaning publics due to higher user entry, and a sustained interest in non-election topics. I discuss these changes as well as the uses and limitations of this model within a research and policy environment increasingly concerned with intervening in networked publics.