Polymarket Scandal: Platform Paid Bloggers for Fake Bets and Winnings
The decentralized event prediction platform Polymarket has found itself at the center of a major scandal. It has been revealed that the company paid content creators to produce videos showcasing fake bets and supposedly large winnings. These videos were actively distributed on social media and used as promotional materials to attract new users.
According to an investigation, dozens of creators were involved in the scheme. I analyzed over 1,100 such videos, as well as internal instructions and interviews with participants. Content creators were paid between $2,000 and $3,000 monthly, while being strictly required not to disclose their commercial partnership with Polymarket. Fake copies of the platform's website were used for filming, displaying non-existent transactions.
A separate marketing agency handled the promotion of these videos. Between December and mid-May, ten sponsored creators published 1,105 videos, which collectively garnered over 140 million views. One of the most active contributors, student George Makihara, posted a video in January where he supposedly won $100,000 by betting that Donald Trump would publicly say the word "McDonald's." According to my calculations, from January to mid-May, Makihara showed 145 bets totaling nearly $410,000 in his videos — all of these transactions were fake.
After the information about the fraudulent activities became public, many creators rushed to delete the videos that compromised them. Polymarket itself also eliminated the duplicate websites used to record the staged materials.
Analytical Commentary
This incident seriously undermines trust in Polymarket as an objective forecasting tool. Using fake bets for marketing is not just an ethical violation but a direct deception of users, which could have legal consequences. Transparency is critically important for prediction markets, and such scandals call into question the legitimacy of the entire industry. Investors and traders should be extremely cautious when drawing conclusions based on data from such sources.