Let’s (re)tweet about racism and sexism: responses to cyber aggression toward Black and Asian women

Online, anyone’s words can easily be amplified – and on Twitter, the platform’s algorithm highlights tweets that gain attention from other users, which can exponentially reinforce a tweet’s popularity. Moreover, retweets can help spread a message well beyond the reach of its original poster. Thus, users’ interactions with posts containing or making reference to racism or sexism both illuminate the ways individuals accept, challenge, or engage with racism and sexism online, and shape how those messages spread. Using an original dataset of 59.5 million tweets, I test how particular features of messages referencing Black and Asian women predict user engagement (retweets, likes, and replies). This analysis further focuses on messages including terms that express racist or sexist content. Generally, messages including covert racist or sexist insults have a modest positive effect on all measures of user engagement (retweets, likes, and replies), which may suggest that social media environments allow individuals the time and opportunity to contend with topics that can be more difficult in-person. Additionally, variations in engagement with tweets that include references to women, Black or Asian individuals implies that users respond differently to messages involving references to and normative images of different racial, ethnic, and gendered identities. This research illuminates how specific manifestations of racialized and gendered language referencing women, Black and Asian people can not only encourage more engagement, but also share, accept, or challenge messages about marginalized identities.