LAUSR

We propose a three-pronged framework to study discourses surrounding social media activism initiated by networked counterpublics: personalized expressions that share stories and support, demands for changes that address systematic problems, and contentions between various actors and perspectives. Situating our analysis in discourses related to sexual violence and gender justice activism on Twitter, Facebook, Instagram, and Reddit, we use supervised machine learning to quantify three discourses—networked acknowledgment, calls to action, and feminism contention—and apply time series analysis to model their interrelations. Results show that networked acknowledgment stimulated both calls to action and feminism contention and that calls to action predicted feminism contention across all platforms. These discourses were more sensitive to real-world events on Twitter and Facebook, but more ephemeral and cyclical on Instagram and more persistent and coupled on Reddit. Our findings speak to the opportunities and challenges in social media activism and underscore cross-platform similarities and differences.