‘Stop Misgendering Me’: Video Shows University Student Arguing With Officer Over Pronouns 54%

By Jennifer58%

7/21/2026, 8:00:00 PM

BS Summary: This article contains 22 faulty reasoning types, including Unattributed Quote, Negativity Bias, and Appeal to Emotion, with Biased Writer Voice as the most egregious example at 40.7% saturation with 229 hits. Analysis detected 1,285 faulty-reasoning hits from 563 analyzed words, generating a BS Score of 52.1% and a BS Rank of 54% (9,921 of 21,160 articles). This article is worse (more manipulative) than 53.10% of the article peer group.

A video of a confrontation between a University of Central Florida student and a campus police officer is circulating online after the student repeatedly corrected the officer’s use of pronouns. 
In a clip making the rounds online, and shared by the California Post on X, the student, who appears to have blue hair, can be seen screaming “she” multiple times while jumping up and down, suggesting those are the pronouns they prefer. 
Shortly after, a campus police officer approaches the scene, and it only gets worse from there. 
Viral police body cam video appears to show a self-described transgender student freaking out on a campus police officer for “misgendering” him  and then being hauled away in cuffs. https://t.co/AAIQURpd9k pic.twitter.com/S3blYehuBG 
- California Post (@californiapost) July 21, 2026 
The Student Reportedly Attends the University of Central Florida 
As the officer approaches the scene, the student yells, “Why do you let those kids get away with this? 
They are not supposed to be here.” 
The New York Post has since identified the student as a 27-year-old who attends the University of Central Florida. 
The officer then replies, “If you don’t lower your voice, I am going to put handcuffs on you and take you to a Baker Act facility.” 
As the officer begins telling the student again to lower their voice, the student interrupts, responding, “F–king try it.” 
The officer then asks, “Are you threatening me?” 
The officer then calls for backup and tells the person on the other end of the line that the student, referring to them as “he,” is having what the officer describes as a breakdown. 
That prompts the student to repeatedly scream, “She!” 
before adding, “Just say the right pronouns.” 
In the bodycam footage, the officer can then be seen pulling out what appears to be pepper spray. 
Realizing what it is, the student then flinches when they see it, and the officer tells them to get on the ground. 
Instead of complying, now the student, who appears to be frightened by the mace, begins yelling at the officer, “Leave me alone!” 
There is no reason for this interaction to have wasted our tax dollars. 
It is not hard to respect other humans, even when you don’t understand. 
It would bother you if someone called you by the wrong name, right? 
SHE is not asking a lot and the officer is not deescalating. 
- JD (@idcojd) July 21, 2026 
Moments later, the student agrees to calm down presumably to avoid being maced, but as the officer continues calling for help and suggests the student be taken away, the officer again refers to the student as “he” prompting the student to once again yell, “She!” 
Later on in the clip, the student can be seen in handcuffs being placed into the back of a police cruiser. 
According to the New York Post , the footage was recorded in February 2025 but wasn’t released until mid-July 2026 by Blue TV, which is why it’s now going viral. 
While many commenters questioned the student’s mental health, others argued the officer “exercised extremely poor judgment,” saying he “purposely inflamed a person in the middle of a mental breakdown.” 
The Daily Dot was unable to independently verify the circumstances surrounding the incident or the claims made in social media posts discussing the video. 
Article reasoning-pattern comparisonThis article: 15.5%Jennifer: 5.4%dailydot.com: 4.3%Confirmation Bias15.5%This article: 0.0%Jennifer: 0.6%dailydot.com: 0.9%Anchoring Bias0.0%This article: 19.4%Jennifer: 5.0%dailydot.com: 4.2%Availability Heuristic19.4%This article: 0.0%Jennifer: 0.5%dailydot.com: 1.2%Representativeness Heuristic0.0%This article: 0.0%Jennifer: 1.6%dailydot.com: 0.7%Hindsight Bias0.0%This article: 0.0%Jennifer: 2.2%dailydot.com: 1.5%Overconfidence Bias0.0%This article: 7.8%Jennifer: 4.7%dailydot.com: 4.9%Framing Effect7.8%This article: 0.0%Jennifer: 0.4%dailydot.com: 0.6%Loss Aversion0.0%This article: 0.0%Jennifer: 0.3%dailydot.com: 0.5%Status Quo Bias0.0%This article: 0.0%Jennifer: 0.0%dailydot.com: 0.2%Sunk Cost Effect0.0%This article: 0.0%Jennifer: 0.4%dailydot.com: 1.1%Optimism Bias0.0%This article: 2.3%Jennifer: 1.5%dailydot.com: 1.3%Pessimism Bias2.3%This article: 23.4%Jennifer: 10.3%dailydot.com: 8.7%Negativity Bias23.4%This article: 0.0%Jennifer: 0.9%dailydot.com: 0.8%Self-Serving Bias0.0%This article: 11.7%Jennifer: 4.2%dailydot.com: 2.6%Fundamental Attribution Error11.7%This article: 3.9%Jennifer: 0.8%dailydot.com: 0.3%Actor-Observer Bias3.9%This article: 0.0%Jennifer: 0.7%dailydot.com: 1.5%In-Group Bias0.0%This article: 0.0%Jennifer: 0.5%dailydot.com: 1.3%Out-Group Homogeneity Bias0.0%This article: 0.0%Jennifer: 2.1%dailydot.com: 2.4%Halo Effect0.0%This article: 0.0%Jennifer: 0.1%dailydot.com: 0.3%Horn Effect0.0%This article: 0.0%Jennifer: 0.4%dailydot.com: 0.1%Dunning-Kruger Effect0.0%This article: 5.3%Jennifer: 0.9%dailydot.com: 0.7%Recency Bias5.3%This article: 2.3%Jennifer: 0.9%dailydot.com: 0.4%Primacy Effect2.3%This article: 0.0%Jennifer: 0.1%dailydot.com: 0.1%Blind-Spot Bias0.0%This article: 0.0%Jennifer: 1.8%dailydot.com: 2.0%Ad Hominem0.0%This article: 0.0%Jennifer: 0.2%dailydot.com: 0.4%Straw Man0.0%This article: 3.4%Jennifer: 2.6%dailydot.com: 1.8%Appeal to Authority3.4%This article: 2.1%Jennifer: 2.9%dailydot.com: 2.3%False Dilemma2.1%This article: 0.0%Jennifer: 0.6%dailydot.com: 0.8%Slippery Slope0.0%This article: 0.0%Jennifer: 0.2%dailydot.com: 0.1%Circular Reasoning0.0%This article: 2.3%Jennifer: 9.2%dailydot.com: 8.9%Hasty Generalization2.3%This article: 0.0%Jennifer: 0.4%dailydot.com: 0.3%Red Herring0.0%This article: 0.0%Jennifer: 3.1%dailydot.com: 2.4%Bandwagon0.0%This article: 23.1%Jennifer: 8.4%dailydot.com: 7.1%Appeal to Emotion23.1%This article: 3.4%Jennifer: 1.1%dailydot.com: 0.9%Begging the Question3.4%This article: 5.3%Jennifer: 2.1%dailydot.com: 1.4%Post Hoc (False Cause)5.3%This article: 0.0%Jennifer: 0.5%dailydot.com: 0.2%Tu Quoque0.0%This article: 0.0%Jennifer: 1.9%dailydot.com: 1.8%Burden of Proof0.0%This article: 2.3%Jennifer: 0.5%dailydot.com: 0.2%Appeal to Nature2.3%This article: 0.0%Jennifer: 0.1%dailydot.com: 0.1%Composition/Division0.0%This article: 5.2%Jennifer: 6.0%dailydot.com: 7.3%Anecdotal5.2%This article: 1.2%Jennifer: 0.0%dailydot.com: 0.1%No True Scotsman1.2%This article: 15.5%Jennifer: 2.6%dailydot.com: 1.9%Ambiguity (Equivocation)15.5%This article: 0.0%Jennifer: 0.0%dailydot.com: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Jennifer: 0.1%dailydot.com: 0.2%Middle Ground0.0%This article: 0.0%Jennifer: 0.0%dailydot.com: 0.3%Personal Incredulity0.0%This article: 0.0%Jennifer: 0.1%dailydot.com: 0.1%Special Pleading0.0%This article: 0.0%Jennifer: 0.2%dailydot.com: 0.2%Genetic Fallacy0.0%This article: 30.0%Jennifer: 6.8%dailydot.com: 4.9%Unattributed Quote30.0%This article: 0.0%Jennifer: 3.7%dailydot.com: 3.4%Quote-first Misdirection0.0%This article: 40.7%Jennifer: 4.9%dailydot.com: 3.9%Biased Writer Voice40.7%This article: 2.1%Jennifer: 1.8%dailydot.com: 1.5%Indoctrination2.1%This article: 0.0%Jennifer: 0.0%dailydot.com: 0.1%Politically Left Leaning Bias0.0%This article: 0.0%Jennifer: 0.5%dailydot.com: 0.8%Politically Right Leaning Bias0.0%This article: 0.0%Jennifer: 1.1%dailydot.com: 2.0%Attempt to Sell a Product or S…0.0%

563 words analyzed.

Speakers

2speakers2.3%attributed speech550writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 12 words • 0.0% coverageWriter's voice • 30 words • 100.0% coverageWriter's voice • 42 words • 100.0% coverageWriter's voice • 16 words • 100.0% coverageWriter's voice • 32 words • 100.0% coverageCalifornia Post (@californiapost) • 7 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 19 words • 100.0% coverageWriter's voice • 7 words • 100.0% coverageWriter's voice • 19 words • 100.0% coverageWriter's voice • 26 words • 100.0% coverageWriter's voice • 19 words • 100.0% coverageWriter's voice • 8 words • 100.0% coverageWriter's voice • 34 words • 100.0% coverageWriter's voice • 8 words • 100.0% coverageWriter's voice • 7 words • 100.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 22 words • 100.0% coverageWriter's voice • 22 words • 100.0% coverageWriter's voice • 13 words • 100.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 12 words • 100.0% coverageJD (@idcojd) • 6 words • 0.0% coverageWriter's voice • 45 words • 100.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 30 words • 0.0% coverageWriter's voice • 29 words • 100.0% coverageWriter's voice • 24 words • 0.0% coverage
0%flagged-word coverage
7 attributed words54% of attributed speech85% writer coverage
0%22.5%45.0%Biased Writer Voice-41.6 ptsWriter: 41.6%California Post (@californiapost): 0.0%0.0%Unattributed Quote-30.7 ptsWriter: 30.7%California Post (@californiapost): 0.0%0.0%Indoctrination-2.2 ptsWriter: 2.2%California Post (@californiapost): 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

Loading…
Loading…
Loading…

Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.