Raw Story93%

'This is power': Biographer reveals dark secret Trump wielded over Lindsey Graham 75%

By Robert Davis75%

7/15/2026, 12:44:58 AM

BS Summary: This article contains 23 faulty reasoning types, including Biased Writer Voice, Hasty Generalization, and Unattributed Quote, with Negativity Bias as the most egregious example at 43.8% saturation with 156 hits. Analysis detected 1,036 faulty-reasoning hits from 356 analyzed words, generating a BS Score of 66.8% and a BS Rank of 75% (5,415 of 21,180 articles). This article is worse (more manipulative) than 74.40% of the article peer group.

'This is power': Biographer reveals dark secret Trump wielded over Lindsey Graham 
One of President Donald Trump's biographers revealed a dark secret that the president may have used to wield power over the late Sen. 
Lindsey Graham (R-SC). 
Graham, who died on Saturday from an aortic dissection after traveling home from Ukraine, was one of Trump's sharpest critics leading up to the president's first election in 2016, but soon became what some described as a supplicant. 
Graham was one of Trump's biggest allies on Capitol Hill and was instrumental in multiple legislative wins. 
He also helped rehabilitate Trump's image following the Jan. 6 insurrection. 
Michael Wolff, who has written four books about Trump, claimed during a new episode of "Inside Trump's Head," a podcast he co-hosts with Joanna Coles of The Daily Beast, that Trump held the key to Graham's public transformation. 
"I don’t think that there’s anybody in Washington at any informed level who was not aware that Lindsey Graham was gay, but Lindsey Graham was also in the closet," Wolff claimed. 
"This was also a discussion within the Trump circle. 
So this was very well known." 
Graham was unmarried and had no children at the time of his death. 
He never publicly confirmed rumors about his sexuality during his time in office, but some people who were around him have raised questions about his behavior in private . 
Wolff noted that Trump didn't think much one way or the other about Graham's sexuality other than it meant "he had something on Lindsey.” 
“Lindsey Graham never said, ‘I’m gay, and if I don’t suck up to Donald Trump, I’m screwed.’ 
Never. 
Of course, never anything like that,” Wolff said. 
“But again, that sense of Donald Trump as this unknown, a constant variable, ‘What is he going to do?’ 
“Even now ... and you haven’t fessed up to this," Wolff continued. 
"I think Lindsey Graham obviously also knew the game. 
This is politics. 
This is power at a very high level. 
People will do anything to you. 
Nothing is off limits in this context.” 
Article reasoning-pattern comparisonThis article: 14.6%Robert Davis: 9.7%Rawstory: 7.8%Confirmation Bias14.6%This article: 0.0%Robert Davis: 0.0%Rawstory: 0.7%Anchoring Bias0.0%This article: 10.4%Robert Davis: 3.4%Rawstory: 4.4%Availability Heuristic10.4%This article: 0.0%Robert Davis: 0.6%Rawstory: 1.1%Representativeness Heuristic0.0%This article: 0.0%Robert Davis: 0.2%Rawstory: 1.0%Hindsight Bias0.0%This article: 2.5%Robert Davis: 2.2%Rawstory: 2.2%Overconfidence Bias2.5%This article: 9.0%Robert Davis: 15.2%Rawstory: 13.6%Framing Effect9.0%This article: 0.0%Robert Davis: 0.3%Rawstory: 0.4%Loss Aversion0.0%This article: 0.0%Robert Davis: 0.0%Rawstory: 0.4%Status Quo Bias0.0%This article: 0.0%Robert Davis: 0.0%Rawstory: 0.1%Sunk Cost Effect0.0%This article: 0.0%Robert Davis: 0.7%Rawstory: 0.7%Optimism Bias0.0%This article: 9.0%Robert Davis: 2.1%Rawstory: 2.8%Pessimism Bias9.0%This article: 43.8%Robert Davis: 20.5%Rawstory: 21.9%Negativity Bias43.8%This article: 0.0%Robert Davis: 0.3%Rawstory: 1.2%Self-Serving Bias0.0%This article: 6.7%Robert Davis: 3.6%Rawstory: 2.9%Fundamental Attribution Error6.7%This article: 0.0%Robert Davis: 0.0%Rawstory: 0.3%Actor-Observer Bias0.0%This article: 0.0%Robert Davis: 2.1%Rawstory: 2.6%In-Group Bias0.0%This article: 0.0%Robert Davis: 1.7%Rawstory: 1.6%Out-Group Homogeneity Bias0.0%This article: 0.0%Robert Davis: 0.0%Rawstory: 0.6%Halo Effect0.0%This article: 0.0%Robert Davis: 0.5%Rawstory: 0.9%Horn Effect0.0%This article: 0.0%Robert Davis: 0.0%Rawstory: 0.0%Dunning-Kruger Effect0.0%This article: 10.7%Robert Davis: 1.3%Rawstory: 1.7%Recency Bias10.7%This article: 10.7%Robert Davis: 1.6%Rawstory: 0.7%Primacy Effect10.7%This article: 0.0%Robert Davis: 0.0%Rawstory: 0.1%Blind-Spot Bias0.0%This article: 0.0%Robert Davis: 5.0%Rawstory: 6.4%Ad Hominem0.0%This article: 4.8%Robert Davis: 0.5%Rawstory: 0.7%Straw Man4.8%This article: 20.2%Robert Davis: 5.5%Rawstory: 3.8%Appeal to Authority20.2%This article: 0.0%Robert Davis: 1.6%Rawstory: 2.9%False Dilemma0.0%This article: 0.0%Robert Davis: 1.8%Rawstory: 1.2%Slippery Slope0.0%This article: 2.2%Robert Davis: 0.1%Rawstory: 0.3%Circular Reasoning2.2%This article: 27.2%Robert Davis: 14.2%Rawstory: 12.0%Hasty Generalization27.2%This article: 4.2%Robert Davis: 0.2%Rawstory: 0.6%Red Herring4.2%This article: 0.0%Robert Davis: 1.0%Rawstory: 0.8%Bandwagon0.0%This article: 8.7%Robert Davis: 8.2%Rawstory: 9.2%Appeal to Emotion8.7%This article: 3.4%Robert Davis: 1.2%Rawstory: 1.5%Begging the Question3.4%This article: 0.0%Robert Davis: 4.2%Rawstory: 3.6%Post Hoc (False Cause)0.0%This article: 0.0%Robert Davis: 0.1%Rawstory: 0.6%Tu Quoque0.0%This article: 8.1%Robert Davis: 1.8%Rawstory: 1.6%Burden of Proof8.1%This article: 0.0%Robert Davis: 0.0%Rawstory: 0.1%Appeal to Nature0.0%This article: 0.0%Robert Davis: 0.0%Rawstory: 0.4%Composition/Division0.0%This article: 21.3%Robert Davis: 4.8%Rawstory: 4.0%Anecdotal21.3%This article: 0.0%Robert Davis: 0.5%Rawstory: 0.3%No True Scotsman0.0%This article: 19.4%Robert Davis: 5.8%Rawstory: 2.4%Ambiguity (Equivocation)19.4%This article: 0.0%Robert Davis: 0.0%Rawstory: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Robert Davis: 0.0%Rawstory: 0.1%Middle Ground0.0%This article: 0.0%Robert Davis: 0.0%Rawstory: 0.2%Personal Incredulity0.0%This article: 0.3%Robert Davis: 0.0%Rawstory: 0.1%Special Pleading0.3%This article: 0.0%Robert Davis: 0.7%Rawstory: 0.4%Genetic Fallacy0.0%This article: 22.2%Robert Davis: 5.5%Rawstory: 5.5%Unattributed Quote22.2%This article: 3.4%Robert Davis: 6.1%Rawstory: 4.2%Quote-first Misdirection3.4%This article: 28.1%Robert Davis: 13.1%Rawstory: 15.4%Biased Writer Voice28.1%This article: 0.0%Robert Davis: 0.5%Rawstory: 3.1%Indoctrination0.0%This article: 0.0%Robert Davis: 5.0%Rawstory: 7.9%Politically Left Leaning Bias0.0%This article: 0.0%Robert Davis: 0.2%Rawstory: 1.0%Politically Right Leaning Bias0.0%This article: 0.0%Robert Davis: 0.0%Rawstory: 0.5%Attempt to Sell a Product or S…0.0%

356 words analyzed.

Speakers

1speaker56%attributed speech158writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 12 words • 100.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 23 words • 100.0% coverageWriter's voice • 3 words • 0.0% coverageWriter's voice • 38 words • 100.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageMichael Wolff • 38 words • 0.0% coverageMichael Wolff • 31 words • 100.0% coverageMichael Wolff • 9 words • 100.0% coverageMichael Wolff • 6 words • 100.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 29 words • 100.0% coverageMichael Wolff • 24 words • 0.0% coverageMichael Wolff • 17 words • 0.0% coverageMichael Wolff • 1 words • 0.0% coverageMichael Wolff • 8 words • 0.0% coverageMichael Wolff • 19 words • 100.0% coverageMichael Wolff • 12 words • 100.0% coverageMichael Wolff • 9 words • 0.0% coverageMichael Wolff • 3 words • 0.0% coverageMichael Wolff • 8 words • 0.0% coverageMichael Wolff • 6 words • 0.0% coverageMichael Wolff • 7 words • 0.0% coverage
Selected voice

Michael Wolff

96%flagged-word coverage
198 attributed words100% of attributed speech79% writer coverage
0%17.5%35.0%Unattributed Quote-19.3 ptsWriter: 32.9%Michael Wolff: 13.6%13.6%Biased Writer Voice-6.4 ptsWriter: 31.6%Michael Wolff: 25.3%25.3%Quote-first Misdirection-7.6 ptsWriter: 7.6%Michael Wolff: 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.