‘Scum’: Trump attacks US states’ efforts to regulate prediction markets 56%

By Roque Planas0%

5/27/2026, 1:25:27 AM

BS Summary: This article contains 30 faulty reasoning types, including Negativity Bias, Recency Bias, and Framing Effect, with Availability Heuristic as the most egregious example at 25% saturation with 135 hits. Analysis detected 1,408 faulty-reasoning hits from 539 analyzed words, generating a BS Score of 53.4% and a BS Rank of 56% (7,476 of 16,813 articles). This article is worse (more manipulative) than 55.50% of the article peer group.

Donald Trump wrote in a social media post on Tuesday it was “critically important” that the federal government retain control over the multibillion-dollar prediction market industry, as he cast a critical eye on state attempts to impose new restrictions. 
The Commodity Futures Trading Commission (CFTC) should retain “exclusive authority” over prediction markets, Trump said. 
Prediction markets allow users to make speculative bets on the outcome of events. 
Their meteoric rise in recent years has attracted attention from state governments, who view trading on prediction markets as gambling by another name, which in many cases would subject the activity to state gaming laws. 
Industry leaders such as Kalshi and Polymarket, which make money by charging fees on transactions, describe prediction markets as a form of derivatives trading. 
Derivatives are contracts that allow traders to make bets on the future value of an asset. 
The federal government has agreed in practice, regulating them through the CFTC, a government agency that oversees derivatives markets with the goal of protecting consumers from fraud. 
Trump has said he “was never much in favor” of prediction markets. 
But he and his family are involved in the industry. 
His media company released a prediction market product last year, and his son Donald Jr has ties to two top prediction market companies, according to the New York Times. 
Prediction market bets have also raised novel ethical problems, since people with insider knowledge and political influence have the ability to predict or shape the current events that now command billions in weekly trading. 
In one prominent case, US army soldier Gannon Ken Van Dyke allegedly used classified information to make more than $400,000 on trades involving the capture of former Venezuelan president Nicolás Maduro after learning of the US military’s plans. 
Federal prosecutors indicted Van Dyke last month on several charges related to insider trading. 
More than a dozen state governments have contemplated curbing prediction markets this year. 
Minnesota became the first state in the country to ban them last week. 
“Prediction markets are designed to be addictive and prey especially on young people and low-income folks,” Minnesota attorney general Keith Ellison said in a statement. 
“They help the ultra-rich get richer and the rest of us get poorer.” 
The CFTC filed a federal lawsuit aiming to overturn the law the day after Minnesota governor Tim Walz signed it. 
In his social media post on Tuesday, Trump, referencing several Democratic political opponents, said: “We cannot have SCUM like Chris Christie, Letitia James, Tim Walz, and JB Pritzker setting the rules! 
Other Countries are after this new form of Financial Market, and we want to remain at the top.” 
He added: “It’s a major industry and we must protect it.” 
Democrats are not the only ones skeptical that prediction markets should be regulated as derivatives. 
The Minnesota bill passed with bipartisan support. 
And Republicans in the state of Utah, whose conservative-dominated government imposes tough anti-gambling laws, are also considering measures to curb betting through prediction markets. 
Prediction markets have surged recently, with weekly trading volume on Kalshi, the most dominant industry player, rising from $100m last year to more than $3bn today, according to an estimate cited by CNBC. 
Confirmation Bias
0%
Anchoring Bias
0%
Availability Heuristic
25%
Representativeness Heuristic
4.5%
Hindsight Bias
0%
Overconfidence Bias
0%
Framing Effect
16.7%
Loss Aversion
0%
Status Quo Bias
4.8%
Sunk Cost Effect
0%
Optimism Bias
3.3%
Pessimism Bias
10.9%
Negativity Bias
23.2%
Self-Serving Bias
4.5%
Fundamental Attribution Error
1.9%
Actor-Observer Bias
0%
In-Group Bias
5.8%
Out-Group Homogeneity Bias
0%
Halo Effect
5%
Horn Effect
0%
Dunning-Kruger Effect
0%
Recency Bias
21.2%
Primacy Effect
9.6%
Blind-Spot Bias
0%
Ad Hominem
5.8%
Straw Man
0%
Appeal to Authority
13.5%
False Dilemma
3.3%
Slippery Slope
0%
Circular Reasoning
0%
Hasty Generalization
12.4%
Red Herring
0%
Bandwagon
3.7%
Appeal to Emotion
16.7%
Begging the Question
6.7%
Post Hoc (False Cause)
0%
Tu Quoque
1.9%
Burden of Proof
0%
Appeal to Nature
0%
Composition/Division
4.5%
Anecdotal
13.4%
No True Scotsman
0%
Ambiguity (Equivocation)
6.5%
Gambler’s Fallacy
0%
Middle Ground
2.8%
Personal Incredulity
0%
Special Pleading
2.8%
Genetic Fallacy
5.4%
Unattributed Quote
11.9%
Quote-first Misdirection
7.6%
Biased Writer Voice
0%
Indoctrination
0%
Politically Left Leaning Bias
0%
Politically Right Leaning Bias
0%
Attempt to Sell a Product or Service
6.1%

539 words analyzed.

Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.