Husband of lefty Canadian who slapped pro-Trump teen wished cancer on prez's family — even 'little kids' ⁠94%

By Chris Nesi⁠71%

7/15/2026, 5:41:57 AM

BS Summary: This article contains 11 faulty reasoning types, including Negativity Bias, Appeal to Emotion, and Politically Right Leaning Bias, with Biased Writer Voice as the most egregious example at 91.3% saturation with 190 hits. Analysis detected 969 faulty-reasoning hits from 208 analyzed words, generating a BS Score of 89.7% and a BS Rank of ⁠94% (1,392 of 21,887 articles). This article is worse (more manipulative) than 93.60% of the article peer group.

The husband of the Canadian woman detained by ICE after slapping a teenager on the Jersey Shore boardwalk for wearing pro-Trump clothing wished for for the deaths of President Trump and his family — including the “little kids.” 
Matthew Geroni, an American citizen married to the boardwalk teen-slapping canuck, Kaitlyn E. 
Tracey, regularly posts unhinged videos on TikTok targeting conservatives. 
In a pair of disturbing videos posted on Geroni’s TikTok account last August, the manchild openly wishes for the president to die. 
“Praying the entire Trump family gets cancer. 
And I mean everybody, like little kids and s–t,” the text superimposed over one video reads as he melodramatically drops to his knees and clasps his hands together. 
In another video posted the next day that appears to use the very same footage of him gyrating around his cluttered apartment like a toddler, Geroni added the text “Praying the next assassination attempt works.” 
Both videos play out to the soundtrack of George Michael’s “Faith.” 
Since his wife’s arrest, the middle-aged man has taken to begging for money on TikTok to put on Tracey’s Ocean County Jail account and retain an immigration lawyer. 
Article reasoning-pattern comparisonThis article: 0.0%Chris Nesi: 4.1%California Post: 4.1%Confirmation Bias0.0%This article: 0.0%Chris Nesi: 1.4%California Post: 1.4%Anchoring Bias0.0%This article: 0.0%Chris Nesi: 4.3%California Post: 4.2%Availability Heuristic0.0%This article: 0.0%Chris Nesi: 1.2%California Post: 1.1%Representativeness Heuristic0.0%This article: 0.0%Chris Nesi: 0.8%California Post: 0.8%Hindsight Bias0.0%This article: 0.0%Chris Nesi: 1.1%California Post: 2.2%Overconfidence Bias0.0%This article: 37.0%Chris Nesi: 9.6%California Post: 10.3%Framing Effect37.0%This article: 0.0%Chris Nesi: 0.0%California Post: 0.9%Loss Aversion0.0%This article: 0.0%Chris Nesi: 0.9%California Post: 0.6%Status Quo Bias0.0%This article: 0.0%Chris Nesi: 0.2%California Post: 0.2%Sunk Cost Effect0.0%This article: 0.0%Chris Nesi: 1.1%California Post: 2.5%Optimism Bias0.0%This article: 0.0%Chris Nesi: 0.8%California Post: 1.5%Pessimism Bias0.0%This article: 85.1%Chris Nesi: 17.0%California Post: 16.0%Negativity Bias85.1%This article: 0.0%Chris Nesi: 4.6%California Post: 2.4%Self-Serving Bias0.0%This article: 13.5%Chris Nesi: 2.6%California Post: 1.5%Fundamental Attribution Error13.5%This article: 0.0%Chris Nesi: 0.4%California Post: 0.2%Actor-Observer Bias0.0%This article: 0.0%Chris Nesi: 0.2%California Post: 1.6%In-Group Bias0.0%This article: 10.6%Chris Nesi: 1.3%California Post: 1.1%Out-Group Homogeneity Bias10.6%This article: 0.0%Chris Nesi: 4.4%California Post: 3.1%Halo Effect0.0%This article: 0.0%Chris Nesi: 2.4%California Post: 0.6%Horn Effect0.0%This article: 0.0%Chris Nesi: 0.0%California Post: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Chris Nesi: 0.8%California Post: 1.6%Recency Bias0.0%This article: 0.0%Chris Nesi: 0.9%California Post: 0.5%Primacy Effect0.0%This article: 0.0%Chris Nesi: 0.1%California Post: 0.0%Blind-Spot Bias0.0%This article: 40.9%Chris Nesi: 5.3%California Post: 2.6%Ad Hominem40.9%This article: 0.0%Chris Nesi: 0.0%California Post: 0.5%Straw Man0.0%This article: 0.0%Chris Nesi: 5.3%California Post: 4.2%Appeal to Authority0.0%This article: 0.0%Chris Nesi: 1.2%California Post: 1.5%False Dilemma0.0%This article: 0.0%Chris Nesi: 0.2%California Post: 0.9%Slippery Slope0.0%This article: 0.0%Chris Nesi: 0.7%California Post: 0.2%Circular Reasoning0.0%This article: 0.0%Chris Nesi: 5.5%California Post: 5.5%Hasty Generalization0.0%This article: 0.0%Chris Nesi: 0.3%California Post: 0.7%Red Herring0.0%This article: 0.0%Chris Nesi: 0.7%California Post: 1.4%Bandwagon0.0%This article: 84.1%Chris Nesi: 8.5%California Post: 8.9%Appeal to Emotion84.1%This article: 0.0%Chris Nesi: 0.5%California Post: 1.1%Begging the Question0.0%This article: 0.0%Chris Nesi: 3.1%California Post: 2.7%Post Hoc (False Cause)0.0%This article: 0.0%Chris Nesi: 0.0%California Post: 0.2%Tu Quoque0.0%This article: 0.0%Chris Nesi: 1.7%California Post: 0.8%Burden of Proof0.0%This article: 0.0%Chris Nesi: 0.0%California Post: 0.2%Appeal to Nature0.0%This article: 0.0%Chris Nesi: 0.0%California Post: 0.2%Composition/Division0.0%This article: 0.0%Chris Nesi: 3.3%California Post: 3.6%Anecdotal0.0%This article: 0.0%Chris Nesi: 0.0%California Post: 0.0%No True Scotsman0.0%This article: 17.8%Chris Nesi: 2.9%California Post: 2.0%Ambiguity (Equivocation)17.8%This article: 0.0%Chris Nesi: 0.0%California Post: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Chris Nesi: 0.0%California Post: 0.1%Middle Ground0.0%This article: 0.0%Chris Nesi: 0.7%California Post: 0.1%Personal Incredulity0.0%This article: 0.0%Chris Nesi: 0.0%California Post: 0.2%Special Pleading0.0%This article: 0.0%Chris Nesi: 0.1%California Post: 0.4%Genetic Fallacy0.0%This article: 29.8%Chris Nesi: 5.0%California Post: 3.2%Unattributed Quote29.8%This article: 11.5%Chris Nesi: 4.4%California Post: 2.1%Quote-first Misdirection11.5%This article: 91.3%Chris Nesi: 18.7%California Post: 13.1%Biased Writer Voice91.3%This article: 0.0%Chris Nesi: 1.1%California Post: 1.4%Indoctrination0.0%This article: 0.0%Chris Nesi: 0.4%California Post: 0.4%Politically Left Leaning Bias0.0%This article: 44.2%Chris Nesi: 2.8%California Post: 3.0%Politically Right Leaning Bias44.2%This article: 0.0%Chris Nesi: 0.2%California Post: 6.0%Attempt to Sell a Product or S…0.0%

208 words analyzed.

Speakers

2speakers27%attributed speech151writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 17 words • 100.0% coverageWriter's voice • 38 words • 100.0% coverageMatthew Geroni • 13 words • 100.0% coverageTracey • 9 words • 100.0% coverageWriter's voice • 22 words • 100.0% coverageWriter's voice • 7 words • 100.0% coverageWriter's voice • 28 words • 100.0% coverageMatthew Geroni • 35 words • 100.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 28 words • 100.0% coverage
Selected voice

Tracey

100%flagged-word coverage
9 attributed words16% of attributed speech93% writer coverage
0%50.0%100.0%Politically Right Leaning +45.0 ptsWriter: 55.0%Tracey: 100.0%100.0%Biased Writer Voice+11.9 ptsWriter: 88.1%Tracey: 100.0%100.0%Unattributed Quote-41.1 ptsWriter: 41.1%Tracey: 0.0%0.0%Quote-first Misdirection-15.9 ptsWriter: 15.9%Tracey: 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.