Does video show Flock cameras being destroyed in US? Here's the truth 10%

By Jordan Liles5%

7/21/2026, 3:00:10 AM

BS Summary: This article contains 20 faulty reasoning types, including Negativity Bias, Confirmation Bias, and Appeal to Emotion, with Appeal to Authority as the most egregious example at 23.5% saturation with 159 hits. Analysis detected 816 faulty-reasoning hits from 677 analyzed words, generating a BS Score of 26.9% and a BS Rank of 10% (19,746 of 21,887 articles). This article is better (less manipulative) than 90.20% of the article peer group.

In July 2026, people shared a video allegedly showing a man destroying Flock surveillance cameras. 
The clip shows several nighttime shots of a man appearing to hold a saw and pushing over cut-through tall black poles with cameras near the top. 
The video only shows the man pushing the cut poles over, not any sawing. 
Flock is a U.S.-based technology company that offers products including motion-activated license plate readers that feature a surveillance camera and solar panel affixed near the top of a pole. 
Flock's website describes the company as "a public safety technology company that builds connected camera, audio and investigative systems to help communities respond to and investigate safety incidents using objective evidence." 
For example, on July 13, an X user posted (archived) the video with the caption, "Vigilantes are smashing Flock cameras, the automated license plate trackers spying on drivers." 
Other users shared the rumor on Facebook (archived), TikTok (archived), X (archived) and YouTube (archived). 
In sum, while vandals have truly cut down Flock cameras in the U.S., according to credible reports, the in-question video showed a man destroying a different company's surveillance technology in London. 
In other words, the video was a genuine (real) clip with no signs of artificial intelligence or other fakery, but social media users posted it with inaccurate text descriptions. 
As a result, we've rated the video miscaptioned. 
A reverse image search indicated the video originated from the FrankyBoy NoULEZ YouTube channel on July 12. 
The channel's "NoULEZ" name references opposition of cameras for London's Ultra Low Emission Zone, a government-sanctioned clean air project launched in 2019. 
The clip also appeared on the user's Instagram and TikTok pages. 
In a TikTok message, FrankyBoyNoULEZ confirmed to Snopes that he originally posted the video and that it shows the destruction of cameras in London. 
In the video, a narrator speaks as ULEZ poles fall to the ground after a man shown in the video cuts each one down. 
The first clip shows a red-and-white triangular sign matching those found in the U.K. 
Speaking in a jocular manner, as if the man were trying to save the poles from falling in a storm, the narrator says in part, "Oh my God. 
The storm in Havering is severe. 
They're really falling over. 
He did try and help that one though." 
The video then cuts to daytime, showing an in-jest sticker affixed to the downed poles and displaying a dotted line for cutting, including the words, "Please do NOT cut this down using a reciprocating saw fitted with an 18 tooth blade as that would be Terrible!!!" 
The same user's Instagram, TikTok and YouTube pages also featured another popular video showing some of the same clips. 
London's ULEZ cameras 
The Transport for London government website says the purpose of ULEZ is to improve air quality by allowing authorities to identify and fine the owners of vehicles that don't meet emissions standards. 
The same website mentions Siemens and Yunex Traffic as companies purportedly involved in the technology's creation. 
(The name of Siemens' Intelligent Traffic Systems division changed to Yunex Traffic in 2021.) 
Opponents of the program have expressed concern about privacy and the financial burden the fines place on commuters. 
In past years, London officials have dealt with individuals vandalizing at least hundreds of ULEZ cameras. 
Those individuals, referenced in reports as "vigilantes," reportedly call themselves "Blade Runners." 
In late June 2026, London Mayor Sadiq Khan, citing a new study, credited ULEZ and clean air policies as leading to "an approximate 40% reduction in the number of estimated deaths linked to air pollution between 2019 and 2024." 
Notes near the bottom of the report specified the findings "does not isolate the impact of ULEZ or any single policy," "does not attribute the reduction to specific policies" and "models the total impact of all factors influencing air pollution, including long-term trends and multiple policy interventions." 
For further reading, we previously investigated whether Flock provides Ring doorbell access to U.S. 
Immigration and Customs Enforcement officers. 
Article reasoning-pattern comparisonThis article: 9.3%Jordan Liles: 2.3%Snopes: 2.4%Confirmation Bias9.3%This article: 0.0%Jordan Liles: 0.9%Snopes: 0.8%Anchoring Bias0.0%This article: 7.4%Jordan Liles: 2.7%Snopes: 3.4%Availability Heuristic7.4%This article: 4.3%Jordan Liles: 1.7%Snopes: 1.3%Representativeness Heuristic4.3%This article: 2.1%Jordan Liles: 0.2%Snopes: 0.2%Hindsight Bias2.1%This article: 7.1%Jordan Liles: 1.5%Snopes: 1.2%Overconfidence Bias7.1%This article: 1.3%Jordan Liles: 0.7%Snopes: 2.4%Framing Effect1.3%This article: 0.0%Jordan Liles: 0.2%Snopes: 0.2%Loss Aversion0.0%This article: 0.0%Jordan Liles: 0.1%Snopes: 0.5%Status Quo Bias0.0%This article: 0.0%Jordan Liles: 0.1%Snopes: 0.1%Sunk Cost Effect0.0%This article: 0.0%Jordan Liles: 0.5%Snopes: 0.5%Optimism Bias0.0%This article: 0.0%Jordan Liles: 0.0%Snopes: 0.4%Pessimism Bias0.0%This article: 10.9%Jordan Liles: 4.0%Snopes: 5.1%Negativity Bias10.9%This article: 0.0%Jordan Liles: 0.0%Snopes: 0.7%Self-Serving Bias0.0%This article: 0.0%Jordan Liles: 0.0%Snopes: 0.6%Fundamental Attribution Error0.0%This article: 0.0%Jordan Liles: 0.0%Snopes: 0.1%Actor-Observer Bias0.0%This article: 0.0%Jordan Liles: 0.0%Snopes: 0.5%In-Group Bias0.0%This article: 1.8%Jordan Liles: 0.1%Snopes: 0.3%Out-Group Homogeneity Bias1.8%This article: 4.6%Jordan Liles: 1.0%Snopes: 0.6%Halo Effect4.6%This article: 0.0%Jordan Liles: 0.0%Snopes: 0.0%Horn Effect0.0%This article: 0.0%Jordan Liles: 0.0%Snopes: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Jordan Liles: 0.8%Snopes: 1.5%Recency Bias0.0%This article: 0.0%Jordan Liles: 0.0%Snopes: 0.5%Primacy Effect0.0%This article: 0.0%Jordan Liles: 0.0%Snopes: 0.1%Blind-Spot Bias0.0%This article: 0.0%Jordan Liles: 1.1%Snopes: 1.5%Ad Hominem0.0%This article: 0.0%Jordan Liles: 0.0%Snopes: 0.4%Straw Man0.0%This article: 23.5%Jordan Liles: 6.1%Snopes: 6.0%Appeal to Authority23.5%This article: 0.0%Jordan Liles: 1.1%Snopes: 0.9%False Dilemma0.0%This article: 0.0%Jordan Liles: 0.4%Snopes: 0.6%Slippery Slope0.0%This article: 1.2%Jordan Liles: 0.5%Snopes: 0.1%Circular Reasoning1.2%This article: 2.4%Jordan Liles: 2.3%Snopes: 3.5%Hasty Generalization2.4%This article: 0.0%Jordan Liles: 0.7%Snopes: 0.7%Red Herring0.0%This article: 6.4%Jordan Liles: 3.0%Snopes: 1.5%Bandwagon6.4%This article: 8.3%Jordan Liles: 4.8%Snopes: 3.0%Appeal to Emotion8.3%This article: 0.0%Jordan Liles: 0.1%Snopes: 0.4%Begging the Question0.0%This article: 5.8%Jordan Liles: 2.7%Snopes: 1.6%Post Hoc (False Cause)5.8%This article: 0.0%Jordan Liles: 0.0%Snopes: 0.2%Tu Quoque0.0%This article: 0.0%Jordan Liles: 0.7%Snopes: 1.6%Burden of Proof0.0%This article: 0.0%Jordan Liles: 0.0%Snopes: 0.2%Appeal to Nature0.0%This article: 0.0%Jordan Liles: 0.0%Snopes: 0.2%Composition/Division0.0%This article: 6.4%Jordan Liles: 3.9%Snopes: 1.8%Anecdotal6.4%This article: 4.6%Jordan Liles: 0.4%Snopes: 0.1%No True Scotsman4.6%This article: 4.3%Jordan Liles: 1.1%Snopes: 1.8%Ambiguity (Equivocation)4.3%This article: 0.0%Jordan Liles: 0.0%Snopes: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Jordan Liles: 0.0%Snopes: 0.1%Middle Ground0.0%This article: 0.0%Jordan Liles: 0.2%Snopes: 0.1%Personal Incredulity0.0%This article: 0.0%Jordan Liles: 0.2%Snopes: 0.1%Special Pleading0.0%This article: 0.0%Jordan Liles: 0.0%Snopes: 0.3%Genetic Fallacy0.0%This article: 0.0%Jordan Liles: 0.8%Snopes: 3.3%Unattributed Quote0.0%This article: 4.1%Jordan Liles: 2.8%Snopes: 2.3%Quote-first Misdirection4.1%This article: 5.0%Jordan Liles: 1.9%Snopes: 2.5%Biased Writer Voice5.0%This article: 0.0%Jordan Liles: 0.4%Snopes: 0.7%Indoctrination0.0%This article: 0.0%Jordan Liles: 0.0%Snopes: 0.2%Politically Left Leaning Bias0.0%This article: 0.0%Jordan Liles: 0.0%Snopes: 0.3%Politically Right Leaning Bias0.0%This article: 0.0%Jordan Liles: 0.1%Snopes: 0.4%Attempt to Sell a Product or S…0.0%

677 words analyzed.

Speakers

3speakers17%attributed speech559writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 9 words • 0.0% coverageWriter's voice • 3 words • 100.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 29 words • 0.0% coverageFlock • 31 words • 0.0% coverageWriter's voice • 28 words • 100.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 31 words • 100.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 46 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 3 words • 0.0% coverageTransport for London • 32 words • 0.0% coverageTransport for London • 16 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageSadiq Khan • 39 words • 0.0% coverageWriter's voice • 47 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverage
Selected voice

Sadiq Khan

100%flagged-word coverage
39 attributed words33% of attributed speech56% writer coverage
0%5.0%10.0%Biased Writer Voice-6.1 ptsWriter: 6.1%Sadiq Khan: 0.0%0.0%Quote-first Misdirection-5.0 ptsWriter: 5.0%Sadiq Khan: 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.