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

ABSTRACT

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.

KEYWORDS:

• Cyberculture
• gender
• identity
• race
• social media
• sociology

Acknowledgements

The author would like to thank the anonymous reviewers, Diane Felmlee, Sarah J. Jackson, Gary J. Adler Jr. and Charles Seguin for their helpful feedback on earlier drafts as well as Jeffrey Inara Iuliano for general assistance. Partial support for this research was provided by Pennsylvania State University’s College of The Liberal Arts Research and Graduate Studies Office Dissertation Support Award, Pennsylvania State University’s African Feminist Initiative Research Award, and the Edna Bennett Pierce Prevention Research Center as a Research Award to Reduce Racism and Promote Antiracism.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 There are important limitations to these classifications of ‘Black’ and ‘Asian’ individuals. These are not universal markers, but generally help identify specific identities and related forms of both oppression and opportunity (Baca Zinn & Dill, 1996).

2 To include one’s comments or otherwise edit a retweet, users have to either select ‘quote tweet’ (an option widely available in late 2020), ‘retweet with comment’ (a limited release option to some users in 2019), or manually copy and paste the post into a new tweet. In this paper, retweets refer to standard retweets (reposted tweets without alteration).

3 This time period is useful to collect messages as there were relatively little racialized or gendered events in the news or popular culture.

4 Several slurs and expletives are censored (using a ‘!’) in this chapter, the original text, downloading strategy, or analyses do not use these censored versions.

5 In analyzing the various forms and benefits of different content analysis methods, Grimmer and Stewart (2013) find that classifiers specifically created for analytic investigations perform better than untested or standardized content analysis applications.

6 However, just because a term was reclaimed by some individuals does not mean that the term cannot also still be used negatively. The term ‘b!tch’ can be used in positive connections, but also can be employed as an insult. For ex., some immigrants may proudly call themselves FOBs (fresh off the boat), yet being called an FOB by an outsider or stranger may still be insulting.

7 The count of associated replies does not differentiate between replies that support or oppose the original post. Thus, reply count only broadly suggests that users want to somehow expand on or react to a tweet’s content.

8 Prior to the natural log transformation, I add a small offset (0.01) to preserve data with no responses in the analyses.

9 I include distinct labels for ‘Racist’ and ‘Sexist’ from ‘Racism’ and ‘Sexism’ given the difference in emotional valence between labelling something racist and something racism. Bring labelled a ‘racist’ or calling someone a ‘racist’ can seem like a highly personal insult. Whereas conversations about racism may avoid individual allegations with or motivations for racist behaviour, using the term racist can easily lead to defensiveness or personal injury claims (for more, watch Smooth, 2008).

10 Despite the possibility of reclaimed insults, everyone who comes across the offensive language may not know or understand the new connotation and still interpret the term to be abusive.

11 When creating the visual, two additional users retweeted the tweet, and so are included in image. Data relating to these users, however, are not included in further analyses nor in the follower network depicted in Figure 2.

12 18,734 is the sum of eight retweeting-users’ followers from the identified tweet (Table 2, Example seven). While some followers may overlap, each retweet would be unique. Thus, even if an account followed multiple of the retweeting-users, they would receive multiple instances of the retweet in their feed.

13 Specifically, ‘Asian’ may be less uniting than its ‘Black’ or ‘African-American’ counterpart.

14 When Asian individuals are brought into mainstream race-relation conversations it is frequently in contrast to Black people, creating additional tension between Black and Asian people (i.e., as ‘model minorities’ Asian individuals are ‘superior’ to other marginalized folk).

Additional information

Notes on contributors

Paulina d. C. Inara Rodis

Paulina d. C. Inara Rodis is a doctoral candidate in sociology at Pennsylvania State University, where she earned her MA in sociology and demography. She is also a 2020–2021 Predoctoral Fellow for Excellence through Diversity in the Annenberg School for Communication at the University of Pennsylvania and a 2021–2022 Office of the Vice Provost for Research (VPR) Postdoctoral Fellow in the Annenberg School for Communication at the University of Pennsylvania. Her research interests focus on the interconnection between social identities and close relationships, racism, sexism, social networks, and social media. She has published work in Sociology of Education, Advances in Gender Research, Sex Roles, and Socius. Email: [email protected].