Guess Which Senator Vows to Nuke Any Reconciliation Bill That Contains the SAVE America Act 90%

By Matt Vespa74%

7/17/2026, 2:00:04 AM

BS Summary: This article contains 25 faulty reasoning types, including Biased Writer Voice, Appeal to Emotion, and Framing Effect, with Negativity Bias as the most egregious example at 65.9% saturation with 269 hits. Analysis detected 1,444 faulty-reasoning hits from 408 analyzed words, generating a BS Score of 82.7% and a BS Rank of 90% (2,362 of 21,887 articles). This article is worse (more manipulative) than 89.20% of the article peer group.

We can always count on Sen. 
Thom Tillis (R-NC) to do the wrong thing. 
He’s been a grumpy cat for months now, ever since President Trump threatened to support a primary challenge against him, which led the North Carolina Republican to abandon his bid for another term. 
Tillis was already becoming a thorn in the side of the Trump White House before that, especially with his antics that nearly derailed the Secretary of War nomination of Pete Hegseth and halted the full-time appointment of Ed Martin as D.C. 
Attorney. 
Now, with parts of the SAVE America Act being mulled for inclusion in reconciliation 3.0, Tillis vows to bring hell on Earth in the Senate to stop it (via The Hill): 
Sen. 
Thom Tillis (N.C.), a retiring Republican who has become one of the most vocal critics of the Trump administration in his party, delivered harsh words for President Trump’s top-priority voter ID legislation on the Senate floor Thursday morning, saying he would stall it if the legislation came again to the Senate. 
“If I see a reconciliation bill come from the House with another failed attempt to confuse this election, I will use every device I have available to slow down the wheels of government until people cop a clue and do the math,” Tillis said, nearly shouting, on the Senate floor. 
Tillis has suggested before that he’ll block efforts to pass that bill if given the chance. 
But his speech Wednesday comes as the House debates a party-line package that includes some provisions of the Safeguard American Voter Eligibility (SAVE America) Act, an election security bill that Trump wants Congress to pass before anything else. 
Versions of the bill have already failed to pass the Senate multiple times. 
Tillis, clearly incensed, argued it would be impossible to put the provisions of the bill into practice even if Congress did manage to enact it, with so little time before the November elections. 
Independent reporter Sara Gonzales asked why Tillis opposes this bill. 
He appeared quite irritated by her question. 
Pass the bill. 
Protect our election. 
This isn’t hard, Senator. 
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Article reasoning-pattern comparisonThis article: 8.6%Matt Vespa: 6.0%Townhall: 5.3%Confirmation Bias8.6%This article: 0.0%Matt Vespa: 0.7%Townhall: 0.7%Anchoring Bias0.0%This article: 0.0%Matt Vespa: 2.5%Townhall: 2.8%Availability Heuristic0.0%This article: 12.5%Matt Vespa: 0.8%Townhall: 0.8%Representativeness Heuristic12.5%This article: 0.0%Matt Vespa: 1.1%Townhall: 0.7%Hindsight Bias0.0%This article: 0.0%Matt Vespa: 0.8%Townhall: 1.7%Overconfidence Bias0.0%This article: 20.6%Matt Vespa: 8.3%Townhall: 11.2%Framing Effect20.6%This article: 0.0%Matt Vespa: 0.4%Townhall: 0.4%Loss Aversion0.0%This article: 0.0%Matt Vespa: 0.2%Townhall: 0.3%Status Quo Bias0.0%This article: 0.0%Matt Vespa: 0.1%Townhall: 0.1%Sunk Cost Effect0.0%This article: 0.0%Matt Vespa: 1.1%Townhall: 1.4%Optimism Bias0.0%This article: 0.0%Matt Vespa: 2.2%Townhall: 1.9%Pessimism Bias0.0%This article: 65.9%Matt Vespa: 14.0%Townhall: 13.5%Negativity Bias65.9%This article: 2.0%Matt Vespa: 1.3%Townhall: 1.8%Self-Serving Bias2.0%This article: 18.1%Matt Vespa: 1.3%Townhall: 1.8%Fundamental Attribution Error18.1%This article: 1.7%Matt Vespa: 0.1%Townhall: 0.1%Actor-Observer Bias1.7%This article: 4.9%Matt Vespa: 4.9%Townhall: 4.6%In-Group Bias4.9%This article: 0.0%Matt Vespa: 2.9%Townhall: 3.2%Out-Group Homogeneity Bias0.0%This article: 0.0%Matt Vespa: 2.0%Townhall: 1.8%Halo Effect0.0%This article: 0.0%Matt Vespa: 1.7%Townhall: 0.9%Horn Effect0.0%This article: 0.0%Matt Vespa: 0.0%Townhall: 0.0%Dunning-Kruger Effect0.0%This article: 3.2%Matt Vespa: 1.0%Townhall: 1.3%Recency Bias3.2%This article: 0.0%Matt Vespa: 0.8%Townhall: 0.6%Primacy Effect0.0%This article: 0.0%Matt Vespa: 0.1%Townhall: 0.0%Blind-Spot Bias0.0%This article: 12.0%Matt Vespa: 6.2%Townhall: 5.0%Ad Hominem12.0%This article: 0.0%Matt Vespa: 1.3%Townhall: 2.0%Straw Man0.0%This article: 3.2%Matt Vespa: 3.7%Townhall: 4.4%Appeal to Authority3.2%This article: 10.3%Matt Vespa: 2.0%Townhall: 1.9%False Dilemma10.3%This article: 7.6%Matt Vespa: 2.2%Townhall: 1.6%Slippery Slope7.6%This article: 0.0%Matt Vespa: 0.3%Townhall: 0.2%Circular Reasoning0.0%This article: 8.6%Matt Vespa: 9.4%Townhall: 9.1%Hasty Generalization8.6%This article: 0.0%Matt Vespa: 0.5%Townhall: 0.6%Red Herring0.0%This article: 0.0%Matt Vespa: 1.4%Townhall: 1.3%Bandwagon0.0%This article: 36.5%Matt Vespa: 8.6%Townhall: 11.1%Appeal to Emotion36.5%This article: 12.3%Matt Vespa: 2.4%Townhall: 2.2%Begging the Question12.3%This article: 8.1%Matt Vespa: 1.9%Townhall: 2.4%Post Hoc (False Cause)8.1%This article: 0.0%Matt Vespa: 0.5%Townhall: 0.6%Tu Quoque0.0%This article: 0.0%Matt Vespa: 2.0%Townhall: 1.5%Burden of Proof0.0%This article: 0.0%Matt Vespa: 0.1%Townhall: 0.1%Appeal to Nature0.0%This article: 0.0%Matt Vespa: 0.2%Townhall: 0.1%Composition/Division0.0%This article: 10.0%Matt Vespa: 2.3%Townhall: 1.4%Anecdotal10.0%This article: 0.0%Matt Vespa: 0.2%Townhall: 0.1%No True Scotsman0.0%This article: 12.5%Matt Vespa: 1.8%Townhall: 2.2%Ambiguity (Equivocation)12.5%This article: 0.0%Matt Vespa: 0.0%Townhall: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Matt Vespa: 0.1%Townhall: 0.1%Middle Ground0.0%This article: 0.0%Matt Vespa: 0.1%Townhall: 0.2%Personal Incredulity0.0%This article: 0.0%Matt Vespa: 0.0%Townhall: 0.2%Special Pleading0.0%This article: 0.0%Matt Vespa: 0.2%Townhall: 0.4%Genetic Fallacy0.0%This article: 12.3%Matt Vespa: 3.0%Townhall: 3.4%Unattributed Quote12.3%This article: 11.3%Matt Vespa: 2.6%Townhall: 3.0%Quote-first Misdirection11.3%This article: 50.0%Matt Vespa: 18.5%Townhall: 16.1%Biased Writer Voice50.0%This article: 4.9%Matt Vespa: 4.3%Townhall: 5.6%Indoctrination4.9%This article: 0.0%Matt Vespa: 0.7%Townhall: 0.2%Politically Left Leaning Bias0.0%This article: 6.1%Matt Vespa: 13.0%Townhall: 13.8%Politically Right Leaning Bias6.1%This article: 10.8%Matt Vespa: 4.9%Townhall: 5.0%Attempt to Sell a Product or S…10.8%

408 words analyzed.

Speakers

3speakers25%attributed speech304writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 15 words • 100.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 8 words • 100.0% coverageWriter's voice • 33 words • 100.0% coverageWriter's voice • 41 words • 100.0% coverageWriter's voice • 1 words • 0.0% coverageWriter's voice • 31 words • 100.0% coverageWriter's voice • 1 words • 0.0% coverageWriter's voice • 51 words • 100.0% coverageThom Tillis • 50 words • 100.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 38 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 33 words • 100.0% coverageSara Gonzales • 10 words • 0.0% coverageWriter's voice • 7 words • 100.0% coverageWriter's voice • 3 words • 0.0% coverageWriter's voice • 3 words • 100.0% coverageWriter's voice • 4 words • 100.0% coverageTownhall • 17 words • 100.0% coverageTownhall • 13 words • 100.0% coverageTownhall • 14 words • 100.0% coverage
Selected voice

Thom Tillis

100%flagged-word coverage
50 attributed words48% of attributed speech98% writer coverage
0%50.0%100.0%Unattributed Quote+100.0 ptsWriter: 0.0%Thom Tillis: 100.0%100.0%Biased Writer Voice-67.1 ptsWriter: 67.1%Thom Tillis: 0.0%0.0%Quote-first Misdirection-15.1 ptsWriter: 15.1%Thom Tillis: 0.0%0.0%Politically Right Leaning -2.6 ptsWriter: 2.6%Thom Tillis: 0.0%0.0%Indoctrination-2.3 ptsWriter: 2.3%Thom Tillis: 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.