Lowest common denominator messaging: issue prioritization in #BlackLivesMatter and #MeToo digital coalitions

Abstract

This study examines what issues are prioritized when social movements form coalitions in digital spaces, focusing on the intersection between #BlackLivesMatter and #MeToo on Twitter. We collected 24,369 tweets containing both hashtags (2020-2021) and employed Latent Dirichlet Allocation topic modeling to analyze tweet text content and network analysis to examine hashtag co-occurrence patterns. Drawing on Strolovitch's theory of selective representation (2006, 2007)-which posits that advocacy organizations prioritize concerns of advantaged constituents over disadvantaged ones-the study classifies content as universal, majority, advantaged group, or disadvantaged group issues. We find a "lowest common denominator" effect where universal and majority issues dominate the discourse (91% of high-visibility hashtags). However, contra Strolovitch, we find that disadvantaged group issues receive greater visibility than advantaged group issues in digital coalitions. The percolation analysis of the hashtag co-occurrence network reveals a hierarchical structure with a 5.5:1 ratio between random and targeted percolation thresholds, characteristic of scale-free networks. K-core decomposition and clique analysis show that single-axis marginalization (e.g., #lgbt, #immigrants) receives greater visibility than intersectional marginalization (e.g., #blacktrans, #blackwomen), with the latter absent from high-visibility categories. These patterns suggest that digital coalitions reproduce patterns of selective representation observed in offline advocacy organizations, not through deliberate strategic choices but through decentralized user behavior shaped by network dynamics and algorithmic amplification. This research advances our understanding of how movement coalitions navigate tensions between broad appeal and addressing complex intersectional grievances while bridging social movement theory with network science principles.