Study: Twitter community clapped back at racist, sexist posts

That racism and sexism are prevalent online is not a surprise. But what UIC professor Ian Kennedy and co-researcher Shahill Parsons found by analyzing tweets after the 2023 NCAA Women’s National Championship basketball game was that commentors used Twitter to speak out against racist and sexist posts they saw on social media.  

“It was nice to see people who are not engaging in contemporary assumptions on social media and instead who were supportive and kind,” said Kennedy, an assistant professor of sociology and a computational social scientist in the UIC College of Liberal Arts and Sciences. 

In 2023, NCAA women’s basketball players Angel Reese, then at Louisiana State University, and Caitlin Clark, then at the University of Iowa, made the same “you can’t see me” competitive gesture in different games during the postseason tournament. 

Reese, who is Black, made the signal during the championship game; Clark, who is white, made the gesture in an earlier game. The response from fans on Twitter to the two players making the same signal was vastly different, as Kennedy and Parsons showed in their research. 

The study shows that there were 36.7% positive tweets and 10.1% negative tweets for Clark after the earlier game. For Reese, there were 39.2% positive tweets and 41.2% negative tweets after the championship game. 

“Although Clark and Reese performed nearly identical gestures at different times, Clark’s behavior was often perceived as more acceptable compared to Reese’s, underscoring how both racial and gender dynamics shape public perceptions,” the authors write. 

But, there was a surprising finding, too.  

“Both tweets with sexist and racist content increased after the game, but so did tweets resisting those narratives.” 

The online support for Reese — evidenced by the number of positive tweets — nearly doubled after the game. Before the game, there were only 18.6% positive tweets for Reese. 

Kennedy and Parsons used an AI model that can sort text and parse meaning to help identify relevant tweets and measure sentiment qualitatively. 

Kennedy said they chose the model, called RoBERTa, to analyze the text of each tweet because it uses bidirectional prediction and can analyze the words that come before and after selected words. 

“We often think linearly because we read linearly. But if you notice, sometimes the meaning of a paragraph isn’t clear until the last word,” Kennedy said. “RoBERTa and the other BERT models are really attentive to that kind of thing, so they’re great at classifying texts.” 

The researchers also looked at how racism and sexism intersect to challenge female athletes on social media. The researchers focused on the words “classiness” and “classless” and how they were used to describe both athletes. When Clark made the gesture, she was called “classy,” while Reese was described as “classless.”  

The construct of “class” in these examples, the researchers assert, is a gendered expectation of how females should appear in public spaces. 

In addition, Kennedy and Parsons found that many people tweeting recognized the combination of racism and sexism.  

“Twitter users are calling out the racism and sexism in the language that we’re using in academia, and so that’s a really exciting moment when we see people who are accessing that same language,” Kennedy said.  

Parsons, Kennedy and fellow authors Quinesha Bentley and Morgan Wack’s paper, “Telling My Sons How Angel Reese Stood Toe to Toe with the KKK and Won: Colorblind Racism and Intersectionality in Sports Discourse on Social Media,” is published in “Sociology of Race and Ethnicity.”