Digital Feminism and Anti-Feminism in Interaction: Relational Dynamics and Hybridization Across Seven Spanish-Speaking Communities on Twitter

Abstract

This study advances a relational and translocal approach to hybridization through a comparative computational analysis of how feminist and anti-feminist discourses coevolve across seven Spanish-speaking Twitter communities. Drawing on 126,978 tweets from the first year of #MeToo, we use time-series analysis, topic modeling, and cross-national comparison to examine how discourses interact and how national contexts shape thematic trajectories. We identify 4 relational patterns—mutual association, feminist-driven dynamic, anti-feminist-driven dynamic, and no association—showing that these movements are not merely reactive, but dynamically shape one another. Topic modeling reveals that specific countries assert discursive leadership, illustrating that linguistic commonality does not imply homogeneous uptake. These findings provide a comparative framework for understanding how polarized movements adapt, compete, and interact in networked publics, with implications for feminist strategizing.