Woman arrested for beating Brooklyn man, 98, with broomstick in fight over political flyers 34%

By Emma Seiwell50% Thomas Tracy42%

6/6/2026, 12:22:13 PM

BS Summary: This article contains 14 faulty reasoning types, including Framing Effect, Self-Serving Bias, and Personal Incredulity, with Halo Effect as the most egregious example at 13.3% saturation with 70 hits. Analysis detected 437 faulty-reasoning hits from 528 analyzed words, generating a BS Score of 41.9% and a BS Rank of 34% (14,561 of 21,887 articles). This article is better (less manipulative) than 66.50% of the article peer group.

Police have arrested the woman accused of beating a 98-year-old man with a broomstick and a metal chair during an argument over leaving a political flyer in a Brooklyn building. 
Tashara Abel, 27, was hit with multiple counts of assault, burglary and weapons possession on Friday for the assault in the building, near the corner of Maple St. and Rogers Ave. in Prospect-Lefferts Gardens, cops said. 
Abel was distributing flyers for the reelection campaign of New York State Committee Member Anthony Beckford for the June 23 Democratic primary when she entered the building around 4 p.m. 
Thursday. 
The building has a strict “no flyers, no posters,” rule, according to a sign attached to its front door. 
As Abel began stuffing mailboxes with the flyers, she got into an argument with the building’s elderly owner, cops said. 
Things quickly escalated to a fight, during which she punched the senior, then beat him with a broomstick and a metal chair, cops said. 
Abel claimed that she didn’t strike the elderly man with a broom or chair and that the nonagenarian started things by pushing her first. 
Abel pushed him back in retaliation, according to court documents. 
Abel lives in Brownsville, about two miles from where the attack took place. 
Cops were able to identify her through a doorbell camera on the block. 
Abel was released without bail at her arraignment in Brooklyn Criminal Court on Saturday on condition that she have no contact with the victim. 
The senior suffered minor injuries and was treated at the scene. 
“The man is my husband. 
He’s shaken up,” the victim’s wife said, declining to give her name. 
“He’s an elderly man. 
It’s just that he’s been assaulted.” 
The attack comes as the NYPD investigates a troubling string of chilling assaults against older New Yorkers. 
Beckford told the Daily News Friday that he was “currently reviewing the situation to better understand the full circumstances.” 
He said he had reached out to the victimized senior to “ensure he is doing well and has the support he needs.” 
“While volunteers may operate independently, anyone associated with my campaign is expected to conduct themselves with respect and professionalism at all times, and if it is determined that an individual connected to my campaign acted inappropriately, appropriate actions will be taken,” Beckford added. 
“We will continue to monitor this matter closely.” 
Neighbors described the 98-year-old victim as a well-known figure in the community, who could always be found sweeping outside his building and offering to help residents on the block. 
“He’s always on the lookout to make sure everybody’s safe,” said one neighbor, who wished not to be named. 
“He’s a real sweetheart.” 
Despite he’s advanced years, the man  who's just two years shy of turning 100  is full of energy and strong, said neighbors, who couldn’t fathom why someone would want to hurt him. 
“(He’s) a really cool community dad,” neighbor Angela Williams, 60, told the Daily News. 
“I can’t understand (the assault). 
He doesn’t bother anyone. 
Why would you beat up someone just for telling you not to leave flyers?” 
Article reasoning-pattern comparisonThis article: 0.0%Emma Seiwell: 2.4%newyorkdailynews: 3.2%Confirmation Bias0.0%This article: 0.0%Emma Seiwell: 0.4%newyorkdailynews: 0.8%Anchoring Bias0.0%This article: 0.0%Emma Seiwell: 3.3%newyorkdailynews: 3.4%Availability Heuristic0.0%This article: 6.4%Emma Seiwell: 1.3%newyorkdailynews: 1.1%Representativeness Heuristic6.4%This article: 0.0%Emma Seiwell: 0.5%newyorkdailynews: 1.0%Hindsight Bias0.0%This article: 1.5%Emma Seiwell: 0.3%newyorkdailynews: 1.3%Overconfidence Bias1.5%This article: 10.8%Emma Seiwell: 5.8%newyorkdailynews: 6.1%Framing Effect10.8%This article: 0.0%Emma Seiwell: 0.5%newyorkdailynews: 0.4%Loss Aversion0.0%This article: 0.0%Emma Seiwell: 0.5%newyorkdailynews: 0.5%Status Quo Bias0.0%This article: 0.0%Emma Seiwell: 0.2%newyorkdailynews: 0.2%Sunk Cost Effect0.0%This article: 0.0%Emma Seiwell: 1.5%newyorkdailynews: 2.8%Optimism Bias0.0%This article: 3.2%Emma Seiwell: 1.6%newyorkdailynews: 1.2%Pessimism Bias3.2%This article: 5.7%Emma Seiwell: 11.4%newyorkdailynews: 9.8%Negativity Bias5.7%This article: 10.6%Emma Seiwell: 1.5%newyorkdailynews: 1.5%Self-Serving Bias10.6%This article: 0.0%Emma Seiwell: 1.5%newyorkdailynews: 1.5%Fundamental Attribution Error0.0%This article: 0.0%Emma Seiwell: 0.3%newyorkdailynews: 0.3%Actor-Observer Bias0.0%This article: 0.0%Emma Seiwell: 0.8%newyorkdailynews: 1.3%In-Group Bias0.0%This article: 0.0%Emma Seiwell: 0.2%newyorkdailynews: 0.4%Out-Group Homogeneity Bias0.0%This article: 13.3%Emma Seiwell: 4.0%newyorkdailynews: 3.9%Halo Effect13.3%This article: 0.0%Emma Seiwell: 0.3%newyorkdailynews: 0.5%Horn Effect0.0%This article: 0.0%Emma Seiwell: 0.0%newyorkdailynews: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Emma Seiwell: 0.9%newyorkdailynews: 1.4%Recency Bias0.0%This article: 6.4%Emma Seiwell: 0.5%newyorkdailynews: 0.4%Primacy Effect6.4%This article: 0.0%Emma Seiwell: 0.0%newyorkdailynews: 0.0%Blind-Spot Bias0.0%This article: 0.0%Emma Seiwell: 1.2%newyorkdailynews: 1.2%Ad Hominem0.0%This article: 0.0%Emma Seiwell: 0.1%newyorkdailynews: 0.2%Straw Man0.0%This article: 0.0%Emma Seiwell: 2.4%newyorkdailynews: 3.2%Appeal to Authority0.0%This article: 2.7%Emma Seiwell: 0.7%newyorkdailynews: 1.1%False Dilemma2.7%This article: 0.0%Emma Seiwell: 0.4%newyorkdailynews: 0.4%Slippery Slope0.0%This article: 0.0%Emma Seiwell: 0.1%newyorkdailynews: 0.1%Circular Reasoning0.0%This article: 0.0%Emma Seiwell: 3.1%newyorkdailynews: 4.1%Hasty Generalization0.0%This article: 0.0%Emma Seiwell: 0.2%newyorkdailynews: 0.3%Red Herring0.0%This article: 0.0%Emma Seiwell: 0.3%newyorkdailynews: 0.4%Bandwagon0.0%This article: 2.3%Emma Seiwell: 10.0%newyorkdailynews: 7.4%Appeal to Emotion2.3%This article: 0.0%Emma Seiwell: 0.4%newyorkdailynews: 0.6%Begging the Question0.0%This article: 0.0%Emma Seiwell: 2.0%newyorkdailynews: 3.4%Post Hoc (False Cause)0.0%This article: 0.0%Emma Seiwell: 0.1%newyorkdailynews: 0.1%Tu Quoque0.0%This article: 0.0%Emma Seiwell: 0.6%newyorkdailynews: 0.5%Burden of Proof0.0%This article: 0.0%Emma Seiwell: 0.2%newyorkdailynews: 0.2%Appeal to Nature0.0%This article: 0.0%Emma Seiwell: 0.2%newyorkdailynews: 0.2%Composition/Division0.0%This article: 0.8%Emma Seiwell: 2.5%newyorkdailynews: 2.6%Anecdotal0.8%This article: 0.0%Emma Seiwell: 0.1%newyorkdailynews: 0.1%No True Scotsman0.0%This article: 0.0%Emma Seiwell: 1.2%newyorkdailynews: 1.6%Ambiguity (Equivocation)0.0%This article: 0.0%Emma Seiwell: 0.0%newyorkdailynews: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Emma Seiwell: 0.0%newyorkdailynews: 0.1%Middle Ground0.0%This article: 10.0%Emma Seiwell: 0.1%newyorkdailynews: 0.1%Personal Incredulity10.0%This article: 0.0%Emma Seiwell: 0.1%newyorkdailynews: 0.1%Special Pleading0.0%This article: 0.0%Emma Seiwell: 0.0%newyorkdailynews: 0.1%Genetic Fallacy0.0%This article: 0.9%Emma Seiwell: 1.9%newyorkdailynews: 2.1%Unattributed Quote0.9%This article: 0.0%Emma Seiwell: 0.9%newyorkdailynews: 1.1%Quote-first Misdirection0.0%This article: 0.0%Emma Seiwell: 3.9%newyorkdailynews: 6.7%Biased Writer Voice0.0%This article: 8.1%Emma Seiwell: 0.9%newyorkdailynews: 3.9%Indoctrination8.1%This article: 0.0%Emma Seiwell: 0.7%newyorkdailynews: 0.8%Politically Left Leaning Bias0.0%This article: 0.0%Emma Seiwell: 0.1%newyorkdailynews: 0.2%Politically Right Leaning Bias0.0%This article: 0.0%Emma Seiwell: 0.3%newyorkdailynews: 0.6%Attempt to Sell a Product or S…0.0%

528 words analyzed.

Speakers

3speakers29%attributed speech375writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 14 words • 0.0% coverageWriter's voice • 30 words • 0.0% coverageWriter's voice • 36 words • 0.0% coverageWriter's voice • 30 words • 0.0% coverageWriter's voice • 1 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageTashara Abel • 24 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 5 words • 100.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageAnthony Beckford • 19 words • 0.0% coverageAnthony Beckford • 22 words • 0.0% coverageAnthony Beckford • 43 words • 100.0% coverageAnthony Beckford • 8 words • 0.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 34 words • 0.0% coverageAngela Williams • 14 words • 0.0% coverageAngela Williams • 5 words • 0.0% coverageAngela Williams • 4 words • 0.0% coverageAngela Williams • 14 words • 0.0% coverage
Selected voice

Angela Williams

100%flagged-word coverage
37 attributed words24% of attributed speech43% writer coverage
0%2.5%5.0%Unattributed Quote-1.3 ptsWriter: 1.3%Angela Williams: 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.