Presearch's "Doppelgänger" is trying to help people discover adult creators rather than use nonconsensual deepfakes. For three days in February, porn star Alix Lynx flew to Miami for her first exclusive creator gathering where she was in full grind mode: shooting Reels and talking strategy with other creators. "It was kind of like SoHo House for OnlyFans girls," she says of the experience, which is called The Circle and drew more than a dozen sex workers, including Remy LaCroix and Forrest Smith. Lynx, who is a former webcam model turned OnlyFans starlet, has a combined 2 million followers across Instagram, TikTok, and X . She joined OnlyFans in 2017 with "the luxury of having my own following," she says, but those numbers haven't always translated to subscriptions. It's why she was in Miami.

The dual nature of these systems naturally influences both users and creators: users' preferences are

BRMs thus naturally induce strategic creators' collective behaviors

On User-Generated Content (UGC) platforms, recommendation algorithms significantly impact creators' motivation to produce content as they compete for

Unqualified images are removed as described in Appendix A.3. Was the "raw" data saved in addition to the preprocessed/cleaned/labeled data (e.g., to

Why Everyone Is Suddenly in a'Very Chinese Time' in Their Lives It's a symbol of what Americans believe their own country has lost. In case you didn't get the memo, everyone is feeling very Chinese these days. Across social media, people are proclaiming that "You met me at a very Chinese time of my life," while performing stereotypically Chinese-coded activities like eating dim sum or wearing the viral Adidas Chinese jacket . The trend blew up so much in recent weeks that celebrities like comedian Jimmy O Yang and influencer Hasan Piker even got in on it. It has now evolved into variations like " Chinamaxxing " (acting increasingly more Chinese) and " u will turn Chinese tomorrow " (a kind of affirmation or blessing).

Clips from creators in Minnesota have become primary evidence in attempts from the right-wing to justify ICE's surge on American cities. In the days since a masked federal agent shot and killed Renee Nicole Good, right-wing creators and influencers like Nick Sortor and Cam Higby have descended on Minneapolis, filming protestors and interviewing Immigration and Customs Enforcement (ICE) agents. So far, they've produced a steady stream of content that appears designed to paint Minneapolis as a lawless city, and the actions of ICE agents like Jonathan Ross, who reportedly shot and killed Good, as self-defense. "HELL YES! ICE just SMASHED a leftist activist's car window in and pulled them out after they interfered in ICE's operations in Minneapolis. MORE OF THIS!" Sortor posted to X on Sunday, "Consequences must be STEEP!"

Search traffic to news sites has already plunged by a third in one year, according to the Reuters Institute for the Study of Journalism. Search traffic to news sites has already plunged by a third in one year, according to the Reuters Institute for the Study of Journalism. Publishers fear AI search summaries and chatbots mean'end of traffic era' Media companies expect web traffic to their sites from online searches to plummet over the next three years, as AI summaries and chatbots change the way consumers use the internet. An overwhelming majority are also planning to encourage their journalists to behave more like YouTube and TikTok content creators this year, as short-form video and audio content continues to boom. The findings are drawn from a new report from the Reuters Institute for the Study of Journalism, which included the views of 280 media leaders from 51 countries.

Recommender systems serve the dual purpose of presenting relevant content to users and helping content creators reach their target audience. The dual nature of these systems naturally influences both users and creators: users' preferences are affected by the items they are recommended, while creators may be incentivized to alter their content to attract more users. We define a model, called user-creator feature dynamics, to capture the dual influence of recommender systems. We prove that a recommender system with dual influence is guaranteed to polarize, causing diversity loss in the system. We then investigate, both theoretically and empirically, approaches for mitigating polarization and promoting diversity in recommender systems. Unexpectedly, we find that common diversity-promoting approaches do not work in the presence of dual influence, while relevancy-optimizing methods like top-$k$ truncation can prevent polarization and improve diversity of the system.