Raw Story93%

‘Going to be brutal’: Trump’s ‘dangerous’ July 4th finale hit with ominous predictions 90%

By Alexander Willis88%

7/4/2026, 7:35:42 PM

BS Summary: This article contains 16 faulty reasoning types, including Anecdotal, Pessimism Bias, and Quote-first Misdirection, with Negativity Bias as the most egregious example at 57.7% saturation with 224 hits. Analysis detected 1,116 faulty-reasoning hits from 388 analyzed words, generating a BS Score of 83% and a BS Rank of 90% (2,238 of 20,930 articles). This article is worse (more manipulative) than 89.30% of the article peer group.

President Donald Trump’s record-breaking fireworks show slated for Saturday night has a growing number of critics worried about the safety of Washington, D.C. residents, with one commentator offering a particularly disturbing prediction as to how the event could take a turn for the worse. 
Trump announced the fireworks show in June, and proudly touted that it would be “the LARGEST FIREWORKS SHOW IN HISTORY” in a post on social media. 
Around 850,000 fireworks are expected to be launched during the show, scheduled to begin around 11 p.m. 
Saturday night. 
A handful of internal National Park Service documents obtained this week by The Washington Post, however, revealed that the fireworks show is expected to cause “dangerous pollution” and “very unhealthy conditions” around the National Mall, conditions so severe that several critics feared the event could turn into a disaster. 
“They'll probably accidentally set the White House on fire,” predicted Nathan Robinson, editor-in-chief of Current Affairs and political commentator, in a social media post on X Saturday. 
The air quality at the fireworks show is expected to be so dire that the Park Service advised that attendees “wear an N95 mask when outdoors,” and that they should “remain indoors as much as possible during and after the show.” 
“Air quality is already bad due to the heat, this is gonna be brutal,” predicted Amanda Carpenter, writer and editor at Protect Democracy and former writer for The Bulwark and CNN contributor, in a social media post on X Saturday. 
Democratic communications strategist Josh Dorner predicted it was unlikely that attendees would adhere to the Park Service’s warning, further exacerbating the danger present at the event. 
“The air is going to be SO BAD because of the enormous quantity of fireworks to be set off tonight in DC that officials cautioned people nearby to wear an N95 mask, which I am guessing approximately no one will do,” Dorner wrote Saturday in a social media post on X. 
And Dean Baker, senior economist at the Center of Economic & Policy Research, argued that a show of such magnitude was simply beyond Trump’s ability to oversee safely. 
“This is waaaay too complicated for an 80-year-old man suffering from dementia to understand,” Baker wrote Saturday in a social media post on X. 
Article reasoning-pattern comparisonThis article: 25.8%Alexander Willis: 6.7%Rawstory: 7.7%Confirmation Bias25.8%This article: 0.0%Alexander Willis: 0.5%Rawstory: 0.8%Anchoring Bias0.0%This article: 11.3%Alexander Willis: 7.1%Rawstory: 4.4%Availability Heuristic11.3%This article: 0.0%Alexander Willis: 0.9%Rawstory: 1.1%Representativeness Heuristic0.0%This article: 0.0%Alexander Willis: 0.6%Rawstory: 1.0%Hindsight Bias0.0%This article: 0.0%Alexander Willis: 2.6%Rawstory: 2.2%Overconfidence Bias0.0%This article: 3.4%Alexander Willis: 18.7%Rawstory: 13.7%Framing Effect3.4%This article: 0.0%Alexander Willis: 1.0%Rawstory: 0.4%Loss Aversion0.0%This article: 0.0%Alexander Willis: 0.6%Rawstory: 0.4%Status Quo Bias0.0%This article: 0.0%Alexander Willis: 0.6%Rawstory: 0.1%Sunk Cost Effect0.0%This article: 0.0%Alexander Willis: 0.5%Rawstory: 0.7%Optimism Bias0.0%This article: 30.4%Alexander Willis: 3.1%Rawstory: 2.8%Pessimism Bias30.4%This article: 57.7%Alexander Willis: 27.6%Rawstory: 21.8%Negativity Bias57.7%This article: 0.0%Alexander Willis: 0.6%Rawstory: 1.1%Self-Serving Bias0.0%This article: 13.9%Alexander Willis: 2.2%Rawstory: 2.9%Fundamental Attribution Error13.9%This article: 6.2%Alexander Willis: 0.9%Rawstory: 0.3%Actor-Observer Bias6.2%This article: 0.0%Alexander Willis: 2.2%Rawstory: 2.5%In-Group Bias0.0%This article: 0.0%Alexander Willis: 0.5%Rawstory: 1.6%Out-Group Homogeneity Bias0.0%This article: 0.0%Alexander Willis: 0.6%Rawstory: 0.6%Halo Effect0.0%This article: 0.0%Alexander Willis: 1.5%Rawstory: 0.9%Horn Effect0.0%This article: 0.0%Alexander Willis: 0.0%Rawstory: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Alexander Willis: 2.8%Rawstory: 1.7%Recency Bias0.0%This article: 0.0%Alexander Willis: 0.4%Rawstory: 0.7%Primacy Effect0.0%This article: 0.0%Alexander Willis: 0.0%Rawstory: 0.1%Blind-Spot Bias0.0%This article: 13.4%Alexander Willis: 3.8%Rawstory: 6.4%Ad Hominem13.4%This article: 0.0%Alexander Willis: 1.0%Rawstory: 0.7%Straw Man0.0%This article: 0.0%Alexander Willis: 4.1%Rawstory: 3.8%Appeal to Authority0.0%This article: 0.0%Alexander Willis: 2.5%Rawstory: 2.9%False Dilemma0.0%This article: 7.0%Alexander Willis: 1.1%Rawstory: 1.2%Slippery Slope7.0%This article: 0.0%Alexander Willis: 0.1%Rawstory: 0.3%Circular Reasoning0.0%This article: 19.3%Alexander Willis: 8.0%Rawstory: 11.9%Hasty Generalization19.3%This article: 0.0%Alexander Willis: 1.8%Rawstory: 0.7%Red Herring0.0%This article: 0.0%Alexander Willis: 1.2%Rawstory: 0.8%Bandwagon0.0%This article: 6.2%Alexander Willis: 11.2%Rawstory: 9.2%Appeal to Emotion6.2%This article: 0.0%Alexander Willis: 1.3%Rawstory: 1.6%Begging the Question0.0%This article: 0.0%Alexander Willis: 2.5%Rawstory: 3.6%Post Hoc (False Cause)0.0%This article: 0.0%Alexander Willis: 0.5%Rawstory: 0.6%Tu Quoque0.0%This article: 6.2%Alexander Willis: 2.3%Rawstory: 1.5%Burden of Proof6.2%This article: 0.0%Alexander Willis: 0.0%Rawstory: 0.1%Appeal to Nature0.0%This article: 0.0%Alexander Willis: 0.4%Rawstory: 0.4%Composition/Division0.0%This article: 34.8%Alexander Willis: 6.3%Rawstory: 3.9%Anecdotal34.8%This article: 0.0%Alexander Willis: 0.1%Rawstory: 0.3%No True Scotsman0.0%This article: 0.0%Alexander Willis: 3.0%Rawstory: 2.4%Ambiguity (Equivocation)0.0%This article: 0.0%Alexander Willis: 0.0%Rawstory: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Alexander Willis: 0.0%Rawstory: 0.1%Middle Ground0.0%This article: 0.0%Alexander Willis: 0.4%Rawstory: 0.2%Personal Incredulity0.0%This article: 0.0%Alexander Willis: 0.0%Rawstory: 0.1%Special Pleading0.0%This article: 0.0%Alexander Willis: 0.5%Rawstory: 0.4%Genetic Fallacy0.0%This article: 7.0%Alexander Willis: 7.5%Rawstory: 5.4%Unattributed Quote7.0%This article: 30.4%Alexander Willis: 5.8%Rawstory: 4.2%Quote-first Misdirection30.4%This article: 14.7%Alexander Willis: 13.4%Rawstory: 15.6%Biased Writer Voice14.7%This article: 0.0%Alexander Willis: 2.0%Rawstory: 3.2%Indoctrination0.0%This article: 0.0%Alexander Willis: 7.3%Rawstory: 8.0%Politically Left Leaning Bias0.0%This article: 0.0%Alexander Willis: 0.8%Rawstory: 0.9%Politically Right Leaning Bias0.0%This article: 0.0%Alexander Willis: 0.5%Rawstory: 0.5%Attempt to Sell a Product or S…0.0%

388 words analyzed.

Speakers

4speakers51%attributed speech192writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 13 words • 100.0% coverageWriter's voice • 44 words • 100.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 49 words • 0.0% coverageNathan Robinson • 27 words • 100.0% coverageWriter's voice • 41 words • 0.0% coverageAmanda Carpenter • 40 words • 100.0% coverageJosh Dorner • 26 words • 0.0% coverageJosh Dorner • 51 words • 100.0% coverageDean Baker • 28 words • 0.0% coverageDean Baker • 24 words • 0.0% coverage
Selected voice

Amanda Carpenter

100%flagged-word coverage
40 attributed words20% of attributed speech55% writer coverage
0%50.0%100.0%Quote-first Misdirection+100.0 ptsWriter: 0.0%Amanda Carpenter: 100.0%100.0%Biased Writer Voice-29.7 ptsWriter: 29.7%Amanda Carpenter: 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.