Raw Story93%

Stephen Miller's 'pained' reaction puts damper on raucous Mar-a-Lago bash: new book 70%

By Alexandria Jacobson0% Investigative Reporter64%

6/24/2026, 4:58:13 AM

BS Summary: This article contains 20 faulty reasoning types, including Unattributed Quote, Appeal to Emotion, and Framing Effect, with Negativity Bias as the most egregious example at 54.2% saturation with 180 hits. Analysis detected 1,301 faulty-reasoning hits from 332 analyzed words, generating a BS Score of 62.9% and a BS Rank of 70% (6,633 of 21,887 articles). This article is worse (more manipulative) than 69.70% of the article peer group.

President Donald Trump rang in the new year at his Mar-a-Lago winter home featuring a multimillion dollar painting auction and high-profile members of his Cabinet dancing and singing along to rap songs, according to a newly released book about Trump’s second term in office. 
New York Times journalists Maggie Haberman and Jonathan Swan open their highly anticipated book, “Regime Change: Inside the Imperial Presidency of Donald Trump,” by painting a picture of the opulent 2026 New Year’s celebration, just days before the U.S. would capture Venezuelan dictator Nicolás Maduro. 
Rapper Vanilla Ice took the stage to perform his 1990 breakout hit, “Ice Ice Baby,” to a range of reactions from Trump’s Cabinet members and top advisers, the authors wrote. 
Kristi Noem, then-Homeland Security Secretary who was frequently called “ICE Barbie” by critics, “threw herself” into the song “with relish, dancing for the cameras, arms pumping,” Swan and Haberman wrote. 
In contrast, Stephen Miller, White House deputy chief of staff for policy and homeland security adviser, “mouthed along to the lyrics, looking stiff and faintly pained, as if caught doing something undignified,” the book said. 
Secretary of State and anticipated 2028 presidential candidate Marco Rubio “put on a show for whoever had their phones out, singing along and dancing in his chair” to the house band's rendition of Pitbull’s hit “Fireball,” according to the book. 
The “highlight” of the evening was a speed painter who created a depiction of Jesus Christ on a canvas. 
As Trump played auctioneer and signed the painting, it would end up being sold for $2.75 million to benefit St. 
Jude Children's Research Hospital and the local sheriff’s department, Haberman and Swan wrote. 
The evening at Mar-a-Lago where Trump “behaved as though no force could touch him” was just foreshadowing of what was to come throughout the first half of 2026, said the book, which revealed the “norm-shattering” efforts from the “most powerful president of our lifetimes.” 
Article reasoning-pattern comparisonThis article: 0.0%Alexandria Jacobson: 23.1%Rawstory: 8.4%Confirmation Bias0.0%This article: 0.0%Alexandria Jacobson: 2.0%Rawstory: 0.8%Anchoring Bias0.0%This article: 19.3%Alexandria Jacobson: 3.5%Rawstory: 4.5%Availability Heuristic19.3%This article: 9.0%Alexandria Jacobson: 3.8%Rawstory: 1.1%Representativeness Heuristic9.0%This article: 13.3%Alexandria Jacobson: 1.7%Rawstory: 1.0%Hindsight Bias13.3%This article: 0.0%Alexandria Jacobson: 2.9%Rawstory: 2.2%Overconfidence Bias0.0%This article: 33.4%Alexandria Jacobson: 18.7%Rawstory: 13.4%Framing Effect33.4%This article: 0.0%Alexandria Jacobson: 0.7%Rawstory: 0.4%Loss Aversion0.0%This article: 0.0%Alexandria Jacobson: 0.7%Rawstory: 0.4%Status Quo Bias0.0%This article: 0.0%Alexandria Jacobson: 0.0%Rawstory: 0.1%Sunk Cost Effect0.0%This article: 0.0%Alexandria Jacobson: 0.0%Rawstory: 0.7%Optimism Bias0.0%This article: 0.0%Alexandria Jacobson: 3.5%Rawstory: 2.9%Pessimism Bias0.0%This article: 54.2%Alexandria Jacobson: 15.2%Rawstory: 21.6%Negativity Bias54.2%This article: 0.0%Alexandria Jacobson: 0.0%Rawstory: 1.2%Self-Serving Bias0.0%This article: 22.6%Alexandria Jacobson: 12.7%Rawstory: 2.8%Fundamental Attribution Error22.6%This article: 0.0%Alexandria Jacobson: 0.0%Rawstory: 0.3%Actor-Observer Bias0.0%This article: 9.0%Alexandria Jacobson: 2.9%Rawstory: 2.5%In-Group Bias9.0%This article: 0.0%Alexandria Jacobson: 2.9%Rawstory: 1.6%Out-Group Homogeneity Bias0.0%This article: 3.9%Alexandria Jacobson: 1.6%Rawstory: 0.6%Halo Effect3.9%This article: 0.0%Alexandria Jacobson: 0.7%Rawstory: 0.9%Horn Effect0.0%This article: 0.0%Alexandria Jacobson: 0.0%Rawstory: 0.0%Dunning-Kruger Effect0.0%This article: 13.3%Alexandria Jacobson: 2.6%Rawstory: 1.8%Recency Bias13.3%This article: 13.6%Alexandria Jacobson: 1.6%Rawstory: 0.7%Primacy Effect13.6%This article: 0.0%Alexandria Jacobson: 0.0%Rawstory: 0.1%Blind-Spot Bias0.0%This article: 9.0%Alexandria Jacobson: 11.7%Rawstory: 6.3%Ad Hominem9.0%This article: 0.0%Alexandria Jacobson: 1.1%Rawstory: 0.7%Straw Man0.0%This article: 0.0%Alexandria Jacobson: 5.1%Rawstory: 4.1%Appeal to Authority0.0%This article: 0.0%Alexandria Jacobson: 6.3%Rawstory: 3.0%False Dilemma0.0%This article: 13.3%Alexandria Jacobson: 7.8%Rawstory: 1.5%Slippery Slope13.3%This article: 13.3%Alexandria Jacobson: 1.9%Rawstory: 0.2%Circular Reasoning13.3%This article: 0.0%Alexandria Jacobson: 22.1%Rawstory: 12.1%Hasty Generalization0.0%This article: 0.0%Alexandria Jacobson: 0.5%Rawstory: 0.7%Red Herring0.0%This article: 0.0%Alexandria Jacobson: 0.0%Rawstory: 0.8%Bandwagon0.0%This article: 35.8%Alexandria Jacobson: 18.9%Rawstory: 9.2%Appeal to Emotion35.8%This article: 0.0%Alexandria Jacobson: 2.4%Rawstory: 1.6%Begging the Question0.0%This article: 13.6%Alexandria Jacobson: 4.7%Rawstory: 3.8%Post Hoc (False Cause)13.6%This article: 0.0%Alexandria Jacobson: 0.0%Rawstory: 0.6%Tu Quoque0.0%This article: 0.0%Alexandria Jacobson: 0.0%Rawstory: 1.7%Burden of Proof0.0%This article: 0.0%Alexandria Jacobson: 0.0%Rawstory: 0.1%Appeal to Nature0.0%This article: 0.0%Alexandria Jacobson: 0.8%Rawstory: 0.5%Composition/Division0.0%This article: 0.0%Alexandria Jacobson: 1.7%Rawstory: 3.9%Anecdotal0.0%This article: 0.0%Alexandria Jacobson: 0.0%Rawstory: 0.3%No True Scotsman0.0%This article: 10.5%Alexandria Jacobson: 2.8%Rawstory: 2.4%Ambiguity (Equivocation)10.5%This article: 0.0%Alexandria Jacobson: 0.0%Rawstory: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Alexandria Jacobson: 0.0%Rawstory: 0.1%Middle Ground0.0%This article: 0.0%Alexandria Jacobson: 0.0%Rawstory: 0.2%Personal Incredulity0.0%This article: 0.0%Alexandria Jacobson: 0.0%Rawstory: 0.1%Special Pleading0.0%This article: 0.0%Alexandria Jacobson: 1.5%Rawstory: 0.4%Genetic Fallacy0.0%This article: 38.6%Alexandria Jacobson: 6.3%Rawstory: 5.3%Unattributed Quote38.6%This article: 32.8%Alexandria Jacobson: 7.5%Rawstory: 4.1%Quote-first Misdirection32.8%This article: 27.4%Alexandria Jacobson: 12.9%Rawstory: 15.7%Biased Writer Voice27.4%This article: 6.0%Alexandria Jacobson: 6.0%Rawstory: 3.2%Indoctrination6.0%This article: 0.0%Alexandria Jacobson: 13.5%Rawstory: 8.0%Politically Left Leaning Bias0.0%This article: 0.0%Alexandria Jacobson: 1.5%Rawstory: 1.0%Politically Right Leaning Bias0.0%This article: 0.0%Alexandria Jacobson: 0.0%Rawstory: 0.5%Attempt to Sell a Product or S…0.0%

332 words analyzed.

Speakers

6speakers55%attributed speech149writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 12 words • 100.0% coverageWriter's voice • 44 words • 100.0% coverageNew York Times journalists Maggie Haberman and Jonathan Swan • 45 words • 0.0% coverageWriter's voice • 30 words • 0.0% coverageKristi Noem • 30 words • 100.0% coverageStephen Miller • 35 words • 100.0% coverageMarco Rubio • 40 words • 100.0% coverageWriter's voice • 19 words • 0.0% coverageDonald Trump • 20 words • 100.0% coverageJude Children's Research Hospital and the local sheriff’s department • 13 words • 0.0% coverageWriter's voice • 44 words • 100.0% coverage
Selected voice

Stephen Miller

100%flagged-word coverage
35 attributed words19% of attributed speech100% writer coverage
0%50.0%100.0%Quote-first Misdirection+70.5 ptsWriter: 29.5%Stephen Miller: 100.0%100.0%Biased Writer Voice+62.4 ptsWriter: 37.6%Stephen Miller: 100.0%100.0%Unattributed Quote-59.1 ptsWriter: 59.1%Stephen Miller: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

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