The owner of the far-right social media account, who sits on a state advisory panel, has drawn attention since the death of a nonbinary student near Tulsa

Far-right activist Chaya Raichik splits her time between California, where she’s registered to vote, and Florida, where she often travels. But the place where she arguably is having the biggest impact these days is Oklahoma, a state she’s visited only once.

Raichik, who operates the social media account Libs of TikTok, has amassed an audience of millions on X, the platform formerly known as Twitter, largely by targeting LGBTQ+ people. Last month, Raichik was appointed to the Oklahoma Library Media Advisory Committee by Republican schools superintendent Ryan Walters, a former history teacher who has been called “the state’s top culture warrior” for his opposition to teachers unions and other conservative targets, including LGBTQ+ students’ rights.

Since her appointment, Raichik has sought to pull books depicting gay and transgender people, as well as sex education, from public school libraries, saying she has found “porn” in various districts. But her growing role in the state has drawn greater attention since Nex Benedict, a 16-year-old nonbinary student, collapsed and died the day after a Feb. 7 fight in a girls’ bathroom at Owasso High School in suburban Tulsa. Family members said Benedict had been bullied for months for being openly nonbinary.

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  • @[email protected]
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    fedilink
    459 months ago

    The thing that really bothers me is the way stochastic terrorists hide behind Brandenburg vs Ohio, from 1969. Back in the 1960s, you didn’t get this kind of thing in front of a huge audience by default, whereas now, social media algorithms selectively show inflammatory speech to the audience most likely to act on it. This means that inflammatory speech which wouldn’t have caused violence in the 1960s is incredibly likely to do so now.

    • @moistclump
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      49 months ago

      Anyone else dumb like me who was wondering

      stochastic: randomly determined; having a random probability distribution or pattern that may be analyzed statistically but may not be predicted precisely.