Mediaite70%

Man Stabs Two In New York City While Allegedly Yelling ‘Allahu Akbar’ 20%

By Kathryn Wilkens41%

7/23/2026, 4:39:25 PM

BS Summary: This article contains 15 faulty reasoning types, including Appeal to Emotion, Framing Effect, and Unattributed Quote, with Negativity Bias as the most egregious example at 15.3% saturation with 55 hits. Analysis detected 457 faulty-reasoning hits from 359 analyzed words, generating a BS Score of 34.2% and a BS Rank of 20% (17,678 of 21,887 articles). This article is better (less manipulative) than 80.80% of the article peer group.

Police arrested a suspect Thursday after two men were stabbed in separate attacks on Manhattan’s Upper West Side, with the NYPD investigating the incidents as a possible hate crime. 
According to authorities, the first attack occurred shortly before 1:30 p.m. near West 84th Street and Central Park West, where a 57-year-old man was stabbed in the torso. 
A second victim, a 50-year-old man, was attacked minutes later near West 86th Street and Amsterdam Avenue, close to both a church and a synagogue. 
NYPD Commissioner Jessica Tisch said the victims, an Asian man and a Jewish man, were transported to Mount Sinai Morningside Hospital. 
One of the men was initially listed in critical condition, though Tisch later said both are expected to survive. 
This afternoon, two people were stabbed in separate attacks on the Upper West Side. 
Both victims  an Asian male and a Jewish male  were removed to Mt. 
Sinai-St. 
Luke’s Hospital, and both are expected to survive. 
Police identified the suspect as 51-year-old Raul Morales , who was taken into custody at an apartment building a few blocks from the second stabbing scene. 
Morales had not been charged as of Thursday afternoon and remained under questioning by detectives. 
Investigators are also working to determine whether Morales used both a blade and a screwdriver during the attacks. 
While officials said the investigation remains in its early stages, Tisch said witness and victim accounts indicate that “Morales yelled ‘Allahu Akbar’ during both attacks,” prompting detectives to evaluate “whether this is a potential hate crime.” 
“While the perpetrator has no known mental health history with the NYPD, the initial investigation suggests that mental health may have been a factor,” Tisch said. 
“At this time, there is no known link between Morales and either of the victims, nor between the victims and each other.” 
New York City Mayor Zohran Mamdani said he had been briefed on the attacks and condemned the violence. 
“These hateful and despicable attacks have no place in our city,” the mayor said. 
Watch a clip of police activity in the area above via CNN. 
Article reasoning-pattern comparisonThis article: 10.0%Kathryn Wilkens: 4.2%Mediaite: 5.0%Confirmation Bias10.0%This article: 0.0%Kathryn Wilkens: 0.8%Mediaite: 0.9%Anchoring Bias0.0%This article: 7.0%Kathryn Wilkens: 4.7%Mediaite: 3.8%Availability Heuristic7.0%This article: 0.0%Kathryn Wilkens: 0.9%Mediaite: 1.0%Representativeness Heuristic0.0%This article: 0.0%Kathryn Wilkens: 0.2%Mediaite: 0.7%Hindsight Bias0.0%This article: 7.2%Kathryn Wilkens: 3.3%Mediaite: 2.1%Overconfidence Bias7.2%This article: 13.4%Kathryn Wilkens: 8.9%Mediaite: 9.1%Framing Effect13.4%This article: 0.0%Kathryn Wilkens: 0.4%Mediaite: 0.4%Loss Aversion0.0%This article: 5.0%Kathryn Wilkens: 1.0%Mediaite: 0.4%Status Quo Bias5.0%This article: 0.0%Kathryn Wilkens: 0.0%Mediaite: 0.1%Sunk Cost Effect0.0%This article: 7.5%Kathryn Wilkens: 0.3%Mediaite: 1.2%Optimism Bias7.5%This article: 7.2%Kathryn Wilkens: 2.9%Mediaite: 1.6%Pessimism Bias7.2%This article: 15.3%Kathryn Wilkens: 11.8%Mediaite: 14.7%Negativity Bias15.3%This article: 0.0%Kathryn Wilkens: 0.9%Mediaite: 1.6%Self-Serving Bias0.0%This article: 0.0%Kathryn Wilkens: 1.2%Mediaite: 1.7%Fundamental Attribution Error0.0%This article: 0.0%Kathryn Wilkens: 0.0%Mediaite: 0.3%Actor-Observer Bias0.0%This article: 0.0%Kathryn Wilkens: 1.6%Mediaite: 2.1%In-Group Bias0.0%This article: 0.0%Kathryn Wilkens: 1.8%Mediaite: 2.0%Out-Group Homogeneity Bias0.0%This article: 0.0%Kathryn Wilkens: 0.1%Mediaite: 1.3%Halo Effect0.0%This article: 0.0%Kathryn Wilkens: 0.4%Mediaite: 0.7%Horn Effect0.0%This article: 0.0%Kathryn Wilkens: 0.0%Mediaite: 0.1%Dunning-Kruger Effect0.0%This article: 0.0%Kathryn Wilkens: 1.8%Mediaite: 2.3%Recency Bias0.0%This article: 0.0%Kathryn Wilkens: 1.4%Mediaite: 0.6%Primacy Effect0.0%This article: 0.0%Kathryn Wilkens: 0.2%Mediaite: 0.1%Blind-Spot Bias0.0%This article: 0.0%Kathryn Wilkens: 0.5%Mediaite: 3.8%Ad Hominem0.0%This article: 0.0%Kathryn Wilkens: 0.1%Mediaite: 1.2%Straw Man0.0%This article: 0.0%Kathryn Wilkens: 5.2%Mediaite: 3.5%Appeal to Authority0.0%This article: 0.0%Kathryn Wilkens: 2.2%Mediaite: 2.4%False Dilemma0.0%This article: 0.0%Kathryn Wilkens: 0.6%Mediaite: 1.3%Slippery Slope0.0%This article: 0.0%Kathryn Wilkens: 0.3%Mediaite: 0.3%Circular Reasoning0.0%This article: 0.0%Kathryn Wilkens: 9.5%Mediaite: 7.6%Hasty Generalization0.0%This article: 0.0%Kathryn Wilkens: 0.6%Mediaite: 0.4%Red Herring0.0%This article: 0.0%Kathryn Wilkens: 0.3%Mediaite: 0.9%Bandwagon0.0%This article: 13.9%Kathryn Wilkens: 5.3%Mediaite: 6.6%Appeal to Emotion13.9%This article: 0.0%Kathryn Wilkens: 1.3%Mediaite: 1.6%Begging the Question0.0%This article: 7.2%Kathryn Wilkens: 2.3%Mediaite: 2.9%Post Hoc (False Cause)7.2%This article: 0.0%Kathryn Wilkens: 0.0%Mediaite: 0.1%Tu Quoque0.0%This article: 0.0%Kathryn Wilkens: 0.9%Mediaite: 1.2%Burden of Proof0.0%This article: 0.0%Kathryn Wilkens: 0.3%Mediaite: 0.1%Appeal to Nature0.0%This article: 0.0%Kathryn Wilkens: 0.3%Mediaite: 0.3%Composition/Division0.0%This article: 0.0%Kathryn Wilkens: 2.0%Mediaite: 3.0%Anecdotal0.0%This article: 0.0%Kathryn Wilkens: 0.0%Mediaite: 0.1%No True Scotsman0.0%This article: 10.0%Kathryn Wilkens: 3.0%Mediaite: 2.8%Ambiguity (Equivocation)10.0%This article: 0.0%Kathryn Wilkens: 0.0%Mediaite: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Kathryn Wilkens: 0.0%Mediaite: 0.1%Middle Ground0.0%This article: 0.0%Kathryn Wilkens: 0.5%Mediaite: 0.3%Personal Incredulity0.0%This article: 0.0%Kathryn Wilkens: 0.0%Mediaite: 0.1%Special Pleading0.0%This article: 0.0%Kathryn Wilkens: 0.0%Mediaite: 0.6%Genetic Fallacy0.0%This article: 13.4%Kathryn Wilkens: 4.2%Mediaite: 3.3%Unattributed Quote13.4%This article: 3.3%Kathryn Wilkens: 4.5%Mediaite: 3.2%Quote-first Misdirection3.3%This article: 3.3%Kathryn Wilkens: 3.5%Mediaite: 8.3%Biased Writer Voice3.3%This article: 0.0%Kathryn Wilkens: 1.6%Mediaite: 1.6%Indoctrination0.0%This article: 0.0%Kathryn Wilkens: 0.9%Mediaite: 2.0%Politically Left Leaning Bias0.0%This article: 0.0%Kathryn Wilkens: 0.2%Mediaite: 1.1%Politically Right Leaning Bias0.0%This article: 3.3%Kathryn Wilkens: 0.6%Mediaite: 1.4%Attempt to Sell a Product or S…3.3%

359 words analyzed.

Speakers

2speakers43%attributed speech203writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 12 words • 100.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageJessica Tisch • 21 words • 0.0% coverageJessica Tisch • 19 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 1 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageJessica Tisch • 36 words • 100.0% coverageJessica Tisch • 26 words • 0.0% coverageJessica Tisch • 22 words • 0.0% coverageZohran Mamdani • 18 words • 0.0% coverageZohran Mamdani • 14 words • 0.0% coverageWriter's voice • 12 words • 100.0% coverage
Selected voice

Jessica Tisch

65%flagged-word coverage
124 attributed words79% of attributed speech42% writer coverage
0%15.0%30.0%Unattributed Quote+23.1 ptsWriter: 5.9%Jessica Tisch: 29.0%29.0%Quote-first Misdirection-5.9 ptsWriter: 5.9%Jessica Tisch: 0.0%0.0%Biased Writer Voice-5.9 ptsWriter: 5.9%Jessica Tisch: 0.0%0.0%Attempt to Sell a Product -5.9 ptsWriter: 5.9%Jessica Tisch: 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.