Black Lives Matter and Me Too are two social movements that have been actively supporting each other and have exponentially increased their power by going viral around the globe. This paper studies what kinds of grievances and demands become prominent at the intersection of these movements as a result of coalition building in digital spaces. Building on the intersectionality theory (Crenshaw, 1990), this study seeks to uncover whether the most salient demands relate to broad themes on which more people concur or specific grievances and demands of intersectional groups (such as Black transgender women), which sound divisive to some. Building on Laclau, we expect to find broader and vaguer themes to dominate more specific themes related to intersectionality. The broader and vaguer a theme, the wider the audience with which it resonates. We hypothesize that messaging in these social movements have used broader themes that were informed by core-periphery structures reflecting power hierarchies mirroring power imbalances that appear at large in a temporally volatile fashion. We test this hypothesis on a large tweet dataset from Jan. 2020 to Dec. 2021 we compiled using Twitter’s API. We applied network analysis on collected data, building co-occurrence networks based on hashtag use with hashtags being divided into those relevant to broad demands (justice, peace, etc.), movement-specific (defunding the police, ending sexual harassment), and identity-specific demands (commemorating Black trans victims of violence); and testing various network metrics. Hashtag frequency distribution and engagement metrics were also used in order to observe the adoption and amplification of broad demands compared to the demands of intersectional groups, notably Black transgender people. Our preliminary findings support our hypothesis. The most salient themes concern racial equality, sexual abuse, and police violence. Higher clustering coefficients were observed within hashtags relating to specific demands in topics such as transgender-related issues alongside lower betweenness centralities for these demands compared to the rest of the hashtag co-occurrence network. Furthermore, hashtags relating to specific demands were associated with more variance in volume and engagement over time, meaning that involvement in the demands of intersectional groups were time and context-dependent.
