Korea’s Amazon Just Exposed 34 Million Users’ Data #shorts 99%

1/30/2026, 9:00:45 AM

Topics: Video
Keywords: Youtube

BS Summary: This video contains 20 faulty reasoning types, including Availability Heuristic, Appeal to Emotion, and Framing Effect, with Negativity Bias as the most egregious example at 77.7% saturation with 185 hits. Analysis detected 978 faulty-reasoning hits from 238 analyzed words, generating a BS Score of 99% and a BS Rank of 99% (274 of 16,813 videos). This video is worse (more manipulative) than 98.40% of the video peer group.

On the morning of November 30th, 2025, 
South Koreans woke up to an unexpected text message. 
It was from Coupon, the biggest e-commerce platform in South Korea and often referred to as the Amazon of South Korea. 
This is what the message looked like in Korean, and it roughly translates to how some of their customers personal information registered with coupon got exposed due to unauthorized access. 
Turns out the sum of their customers whose personal data was stolen was over 33.7 million South Korean customers, which is nearly 2/3 of the entire Korean population of 52 million. 
But here's the part that makes story even more alarming. 
This data breach began on June 24th, 2025, nearly 5 months before anyone knew it was happening. 
That means for almost half a year, someone had unfettered access to the personal information of 33.7 million South Koreans. 
They could see where you lived. 
They could see what you bought. 
They could see the security codes you entered to get into your apartment building. 
And Kong, one of the most valuable companies, not just in South Korea, but also in Asia with an annual revenue of $30 billion US, apparently had no idea it was happening. 
So, how could something like this happen in the first place? 
And how did this lead to the US government eventually getting involved in the issue? 
Confirmation Bias
0%
Anchoring Bias
2.9%
Availability Heuristic
47.9%
Representativeness Heuristic
8.8%
Hindsight Bias
7.1%
Overconfidence Bias
21.8%
Framing Effect
39.9%
Loss Aversion
0%
Status Quo Bias
0%
Sunk Cost Effect
0%
Optimism Bias
0%
Pessimism Bias
4.2%
Negativity Bias
77.7%
Self-Serving Bias
0%
Fundamental Attribution Error
13.4%
Actor-Observer Bias
0%
In-Group Bias
0%
Out-Group Homogeneity Bias
0%
Halo Effect
22.3%
Horn Effect
0%
Dunning-Kruger Effect
0%
Recency Bias
4.2%
Primacy Effect
2.9%
Blind-Spot Bias
0%
Ad Hominem
0%
Straw Man
0%
Appeal to Authority
28.6%
False Dilemma
13.4%
Slippery Slope
0%
Circular Reasoning
0%
Hasty Generalization
13%
Red Herring
0%
Bandwagon
0%
Appeal to Emotion
44.5%
Begging the Question
0%
Post Hoc (False Cause)
21.8%
Tu Quoque
0%
Burden of Proof
4.6%
Appeal to Nature
0%
Composition/Division
0%
Anecdotal
0%
No True Scotsman
0%
Ambiguity (Equivocation)
26.9%
Gambler’s Fallacy
0%
Middle Ground
0%
Personal Incredulity
4.6%
Special Pleading
0%
Genetic Fallacy
0%
Unattributed Quote
0%
Quote-first Misdirection
0%
Biased Writer Voice
0%
Indoctrination
0%
Politically Left Leaning Bias
0%
Politically Right Leaning Bias
0%
Attempt to Sell a Product or Service
0%

238 words analyzed.

Analysis

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