A Viral Video Claims Los Angeles Paid a Chinese-Owned Hotel $154 a Night Per Room Regardless of Occupancy  the Owner Has a DOJ Conviction for Bribery 64%

By Sayantan47%

7/24/2026, 4:47:41 AM

BS Summary: This article contains 21 faulty reasoning types, including Burden of Proof, Unattributed Quote, and Negativity Bias, with Confirmation Bias as the most egregious example at 45.1% saturation with 255 hits. Analysis detected 1,516 faulty-reasoning hits from 565 analyzed words, generating a BS Score of 58.6% and a BS Rank of 64% (7,922 of 21,887 articles). This article is worse (more manipulative) than 63.80% of the article peer group.

A video shared to X by @WallStreetApes shows a man standing outside the L.A. 
Grand Hotel in downtown Los Angeles, describing a city lease arrangement he said paid the hotel’s owner, Shenzhen New World, $154 a night per room, regardless of occupancy. 
He said the city leased the building starting in 2020 under Project Roomkey, later folded into Mayor Karen Bass’s Inside Safe program, and claimed the city paid roughly $100 million to the company in total. 
He also said that the occupancy at one point fell to 24 percent of rooms while the flat per-room rate continued. 
WOW ? 
Los Angeles Democrats paid $100 million dollars to a private company owned by a Chinese billionaire for a homeless hotel 
Democrats also agreed to pay for ALL rooms regardless if anyone was housed 
The deal was so shady the Chinese billionaire fled to China when the FBI… pic.twitter.com/TI8nMdi7U9 
- Wall Street Apes (@WallStreetApes) July 23, 2026 
The man also alleged without evidence that the arrangement functioned as a money laundering scheme benefiting Democratic donors and described Wei Huang as a Democratic donor. 
The claims couldn’t be independently verified by the Daily Dot . 
Reacting to the video, one commenter argued that fraud tied to city contracts frequently gets “laundered and smurfed into campaign donations,” without offering supporting evidence for that specific characterization. 
Another user wrote that they had experienced the corruption firsthand, “I notice this a lot in California. 
They don’t even hide their obvious corruption because the LA Times and other media never bother to investigate any of it and the AG is part of their network of corruption.” 
The owner of L.A. 
Grand Hotel is Wei Huang, a billionaire who is also the owner of Shenzhen New World Group, according to the Justice Department . 
His company purchased the L.A. 
Grand Hotel in 2010. 
Prosecutors said Huang provided more than $500,000 to former Los Angeles City Councilman Jose Huizar, allegedly to help settle a sexual harassment lawsuit against Huizar in exchange for Huizar’s support for the company’s planned 77-story tower nearby. 
I notice this a lot in California. 
They don’t even hide their obvious corruption because the LA Times and other media never bother to investigate any of it and the AG is part of their network of corruption. 
- áine (@jillsdeal) July 23, 2026 
Shenzhen New World was convicted in November 2022 on charges including honest services wire fraud and bribery, and was fined $4 million and placed on five years’ probation in May 2023, per the same DOJ release. 
Huang was also charged but has not appeared in court. 
The Justice Department considers him a fugitive and believes he is living in China. 
A third commenter raised additional questions about Huang’s immigration history, asking whether he obtained a U.S. passport tied to his investments and whether he had children born in the country during his time here. 
The Daily Dot could not independently verify the total lease payments beyond $8.7 million documented through mid-2023, the money laundering allegations made in the video, the $100 million payment claim, or any direct financial connection between Wei Huang and Democratic campaign donations. 
Confirmed details about Shenzhen New World’s criminal conviction are drawn from Justice Department press releases. 
Article reasoning-pattern comparisonThis article: 45.1%Sayantan: 6.9%dailydot.com: 4.3%Confirmation Bias45.1%This article: 3.7%Sayantan: 1.4%dailydot.com: 0.9%Anchoring Bias3.7%This article: 10.1%Sayantan: 6.4%dailydot.com: 4.2%Availability Heuristic10.1%This article: 0.0%Sayantan: 1.2%dailydot.com: 1.1%Representativeness Heuristic0.0%This article: 0.0%Sayantan: 0.4%dailydot.com: 0.7%Hindsight Bias0.0%This article: 2.7%Sayantan: 3.2%dailydot.com: 1.6%Overconfidence Bias2.7%This article: 4.8%Sayantan: 3.6%dailydot.com: 4.9%Framing Effect4.8%This article: 0.0%Sayantan: 0.9%dailydot.com: 0.6%Loss Aversion0.0%This article: 0.0%Sayantan: 0.1%dailydot.com: 0.5%Status Quo Bias0.0%This article: 0.0%Sayantan: 0.2%dailydot.com: 0.2%Sunk Cost Effect0.0%This article: 0.0%Sayantan: 0.8%dailydot.com: 1.2%Optimism Bias0.0%This article: 0.0%Sayantan: 1.9%dailydot.com: 1.4%Pessimism Bias0.0%This article: 26.5%Sayantan: 11.8%dailydot.com: 8.7%Negativity Bias26.5%This article: 0.0%Sayantan: 0.2%dailydot.com: 0.8%Self-Serving Bias0.0%This article: 4.6%Sayantan: 1.3%dailydot.com: 2.5%Fundamental Attribution Error4.6%This article: 0.0%Sayantan: 0.0%dailydot.com: 0.3%Actor-Observer Bias0.0%This article: 3.5%Sayantan: 2.9%dailydot.com: 1.5%In-Group Bias3.5%This article: 3.5%Sayantan: 1.5%dailydot.com: 1.3%Out-Group Homogeneity Bias3.5%This article: 0.0%Sayantan: 0.0%dailydot.com: 2.4%Halo Effect0.0%This article: 0.0%Sayantan: 0.0%dailydot.com: 0.2%Horn Effect0.0%This article: 0.0%Sayantan: 0.0%dailydot.com: 0.1%Dunning-Kruger Effect0.0%This article: 0.0%Sayantan: 0.5%dailydot.com: 0.7%Recency Bias0.0%This article: 0.0%Sayantan: 0.3%dailydot.com: 0.4%Primacy Effect0.0%This article: 0.0%Sayantan: 0.1%dailydot.com: 0.1%Blind-Spot Bias0.0%This article: 0.0%Sayantan: 1.6%dailydot.com: 1.9%Ad Hominem0.0%This article: 0.0%Sayantan: 0.1%dailydot.com: 0.4%Straw Man0.0%This article: 10.4%Sayantan: 3.4%dailydot.com: 1.9%Appeal to Authority10.4%This article: 0.0%Sayantan: 2.2%dailydot.com: 2.2%False Dilemma0.0%This article: 0.0%Sayantan: 0.5%dailydot.com: 0.8%Slippery Slope0.0%This article: 0.0%Sayantan: 0.0%dailydot.com: 0.1%Circular Reasoning0.0%This article: 23.7%Sayantan: 16.4%dailydot.com: 8.9%Hasty Generalization23.7%This article: 4.6%Sayantan: 0.6%dailydot.com: 0.3%Red Herring4.6%This article: 3.5%Sayantan: 1.1%dailydot.com: 2.4%Bandwagon3.5%This article: 5.1%Sayantan: 4.7%dailydot.com: 7.1%Appeal to Emotion5.1%This article: 0.0%Sayantan: 0.6%dailydot.com: 0.9%Begging the Question0.0%This article: 2.7%Sayantan: 2.1%dailydot.com: 1.5%Post Hoc (False Cause)2.7%This article: 0.0%Sayantan: 0.1%dailydot.com: 0.2%Tu Quoque0.0%This article: 38.1%Sayantan: 5.1%dailydot.com: 1.9%Burden of Proof38.1%This article: 0.0%Sayantan: 0.6%dailydot.com: 0.2%Appeal to Nature0.0%This article: 0.0%Sayantan: 0.0%dailydot.com: 0.1%Composition/Division0.0%This article: 14.9%Sayantan: 14.3%dailydot.com: 7.4%Anecdotal14.9%This article: 0.0%Sayantan: 0.7%dailydot.com: 0.1%No True Scotsman0.0%This article: 12.0%Sayantan: 2.4%dailydot.com: 1.9%Ambiguity (Equivocation)12.0%This article: 0.0%Sayantan: 0.0%dailydot.com: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Sayantan: 0.0%dailydot.com: 0.3%Middle Ground0.0%This article: 0.0%Sayantan: 0.2%dailydot.com: 0.3%Personal Incredulity0.0%This article: 0.0%Sayantan: 0.0%dailydot.com: 0.1%Special Pleading0.0%This article: 0.0%Sayantan: 0.4%dailydot.com: 0.2%Genetic Fallacy0.0%This article: 27.8%Sayantan: 7.9%dailydot.com: 5.1%Unattributed Quote27.8%This article: 4.8%Sayantan: 4.0%dailydot.com: 3.4%Quote-first Misdirection4.8%This article: 16.1%Sayantan: 2.1%dailydot.com: 4.0%Biased Writer Voice16.1%This article: 0.0%Sayantan: 0.4%dailydot.com: 1.6%Indoctrination0.0%This article: 0.0%Sayantan: 0.1%dailydot.com: 0.1%Politically Left Leaning Bias0.0%This article: 0.0%Sayantan: 0.8%dailydot.com: 0.8%Politically Right Leaning Bias0.0%This article: 0.0%Sayantan: 0.6%dailydot.com: 1.9%Attempt to Sell a Product or S…0.0%

565 words analyzed.

Speakers

2speakers25%attributed speech422writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 27 words • 100.0% coverage@WallStreetApes • 14 words • 0.0% coverageWriter's voice • 28 words • 100.0% coverageWriter's voice • 35 words • 100.0% coverageWriter's voice • 21 words • 100.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 20 words • 100.0% coverageWriter's voice • 13 words • 100.0% coverageWriter's voice • 15 words • 100.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 26 words • 100.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 29 words • 100.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 31 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageJustice Department • 23 words • 0.0% coverageJustice Department • 5 words • 0.0% coverageJustice Department • 4 words • 0.0% coverageJustice Department • 37 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 31 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageJustice Department • 36 words • 0.0% coverageJustice Department • 10 words • 0.0% coverageJustice Department • 14 words • 0.0% coverageWriter's voice • 34 words • 100.0% coverageWriter's voice • 42 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverage
Selected voice

@WallStreetApes

100%flagged-word coverage
14 attributed words9.8% of attributed speech86% writer coverage
0%20.0%40.0%Unattributed Quote-37.2 ptsWriter: 37.2%@WallStreetApes: 0.0%0.0%Biased Writer Voice-21.6 ptsWriter: 21.6%@WallStreetApes: 0.0%0.0%Quote-first Misdirection-6.4 ptsWriter: 6.4%@WallStreetApes: 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.