Words and Action: Modeling Linguistic Leadership in #BlackLivesMatter Communities

Abstract

In the wake of the 2024 US presidential election, pundits on both the left and the right pointed to a conservative backlash against "woke politics" to explain the election's outcome. These politics, rooted in substantive beliefs about equity and justice--and particularly racial justice--owe their most recent rise to prominence to the Black Lives Matter (BLM) movement. A significant body of work, both qualitative and quantitative, has documented how BLM was able to move these beliefs from the margin to the mainstream. In this paper, we focus on the words that index these beliefs, devising a novel method of modeling semantic leadership across a set of communities associated with the BLM movement that is informed by domain-specific theory about Black Twitter. We describe our bespoke approaches to time-binning, community clustering, and connecting communities over time, as well as our adaptation of state-of-the-art approaches to semantic change detection and semantic leadership induction. We find evidence at scale of the leadership role of BLM activists and progressives, as well as of Black celebrities. We also find evidence of sustained conservative engagement with this discourse, suggesting an alternative explanation for how we have arrived at the present political moment.