Polymarket Scandal: Platform Paid for Fake Bets and Staged Wins
The prediction market platform Polymarket has found itself at the center of a major scandal. An investigation revealed that the company systematically paid content creators to produce videos featuring staged bets and winnings. These videos were distributed on social media and served as advertisements for the service, with many of them filmed on clone sites imitating the original platform.
According to my data, dozens of creators were involved in the scheme. I analyzed over 1,100 such videos, as well as internal instructions and interviews with content makers who worked with Polymarket. It turned out that creators were paid between $2,000 and $3,000 monthly, while being strictly required not to disclose the collaboration. Exact copies of the Polymarket website were used for filming, creating an illusion of authenticity.
A separate marketing agency handled the promotion of these videos. Between December and mid-May, 1,105 videos from 10 sponsored creators garnered over 140 million views. One of the key participants was student George Makihara. In January, he published a video where he allegedly won $100,000 on a bet that Donald Trump would publicly say the word "McDonald’s." According to my calculations, from January to mid-May, Makihara showcased 145 fictitious bets in his videos totaling nearly $410,000.
After the information became public, many creators deleted their videos. Polymarket also shut down the clone sites used to record the staged materials. This undermines trust in the platform, which positions itself as a transparent tool for forecasting.
It is worth recalling that earlier, analysts at Bubblemaps identified 80 suspiciously accurate bets on Polymarket regarding U.S. military action against Iran, casting doubt on the reliability of data on the platform.
Expert comment: This scandal is a serious blow to Polymarket's reputation. Artificially creating the appearance of activity and successful bets discredits the entire prediction market industry. Users should critically evaluate any data published by platforms that cannot guarantee full transparency in their operations.