Expert’s hundreds of warnings foretold Venezuelan quake disaster - The Japan Times ⁠24%

By Fabiola Zerpa⁠16%

7/13/2026, 1:00:00 AM

BS Summary: This article contains 23 faulty reasoning types, including Confirmation Bias, Negativity Bias, and Post Hoc (False Cause), with Biased Writer Voice as the most egregious example at 25.5% saturation with 74 hits. Analysis detected 686 faulty-reasoning hits from 290 analyzed words, generating a BS Score of 36.9% and a BS Rank of ⁠24% (15,716 of 20,516 articles). This article is better (less manipulative) than 76.60% of the article peer group.

Expert’s hundreds of warnings foretold Venezuelan quake disaster 
A Venezuelan flag flies above the rubble as rescuers search collapsed buildings in Caraballeda, La Guaira state, Venezuela, on Sunday. 
The devastation caused by Venezuela’s twin earthquakes on June 24 shocked the world, but not Carlos Genatios. 
The former science minister and engineer had spent years warning that flawed reconstruction after the deadly 1999 La Guaira mudslides, combined with continued building in high-risk areas, left the region vulnerable. 
The disaster is exactly what he feared. 
Soft alluvial soils, proximity to the San Sebastian Fault and widespread disregard for building codes — including regulations he helped draft nearly three decades ago — combined to magnify two minutes of violent shaking. 
How Putin turned Japan into a den of spies 
China’s demand for Soviet-style apartments shows limits to revival 
Japan successfully launches and lands reusable rocket 
Japanese payment processor’s collapse hits banks and restaurants 
Is ramen soup? 
An inquiry. 
Global demand is reshaping secondhand fashion in Japan 
Japan’s largest exhibition of women photographers rights a wrong in cultural history 
Over 70% of accommodations in Japan say they are understaffed amid tourism influx 
‘Princess Mononoke’ comes alive in Super Kabuki staging 
Okinawa’s prized seaweed under threat as oceans warm 
Kabukicho: Tokyo’s ‘stadium of desire’ 
Why Japan’s utility poles won’t be disappearing anytime soon 
Sompo looks beyond insurance to focus on well-being 
Sponsored contents planned and edited by JT Media Enterprise Division. 
広告出稿に関するおといあわせはこちらまで 
U.S., Iran trade wave of strikes while disputing status of Hormuz 
Lindsey Graham’s death deprives Ukraine of key Trump whisperer 
South Korea asks the North to help search for missing sailor 
Typhoon Bavi batters eastern China, threatens days of heavy rain 
Article reasoning-pattern comparisonThis article: 22.1%Fabiola Zerpa: 5.5%The Japan Times: 3.6%Confirmation Bias22.1%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 1.9%Anchoring Bias0.0%This article: 10.7%Fabiola Zerpa: 3.4%The Japan Times: 4.7%Availability Heuristic10.7%This article: 5.9%Fabiola Zerpa: 1.5%The Japan Times: 1.3%Representativeness Heuristic5.9%This article: 9.3%Fabiola Zerpa: 11.2%The Japan Times: 0.6%Hindsight Bias9.3%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 2.0%Overconfidence Bias0.0%This article: 17.9%Fabiola Zerpa: 5.3%The Japan Times: 14.4%Framing Effect17.9%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.9%Loss Aversion0.0%This article: 3.1%Fabiola Zerpa: 0.8%The Japan Times: 1.3%Status Quo Bias3.1%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.2%Sunk Cost Effect0.0%This article: 9.3%Fabiola Zerpa: 2.3%The Japan Times: 4.7%Optimism Bias9.3%This article: 3.1%Fabiola Zerpa: 0.8%The Japan Times: 3.3%Pessimism Bias3.1%This article: 21.7%Fabiola Zerpa: 5.4%The Japan Times: 11.5%Negativity Bias21.7%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.8%Self-Serving Bias0.0%This article: 10.7%Fabiola Zerpa: 2.7%The Japan Times: 0.9%Fundamental Attribution Error10.7%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.1%Actor-Observer Bias0.0%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 1.1%In-Group Bias0.0%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.8%Out-Group Homogeneity Bias0.0%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 1.5%Halo Effect0.0%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.1%Horn Effect0.0%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.0%Dunning-Kruger Effect0.0%This article: 3.1%Fabiola Zerpa: 0.8%The Japan Times: 2.2%Recency Bias3.1%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.9%Primacy Effect0.0%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.0%Blind-Spot Bias0.0%This article: 5.9%Fabiola Zerpa: 1.5%The Japan Times: 0.2%Ad Hominem5.9%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.1%Straw Man0.0%This article: 6.9%Fabiola Zerpa: 4.4%The Japan Times: 5.6%Appeal to Authority6.9%This article: 3.1%Fabiola Zerpa: 0.8%The Japan Times: 1.6%False Dilemma3.1%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 1.0%Slippery Slope0.0%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.2%Circular Reasoning0.0%This article: 10.3%Fabiola Zerpa: 2.6%The Japan Times: 4.5%Hasty Generalization10.3%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.3%Red Herring0.0%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.7%Bandwagon0.0%This article: 17.6%Fabiola Zerpa: 5.2%The Japan Times: 3.6%Appeal to Emotion17.6%This article: 2.4%Fabiola Zerpa: 0.6%The Japan Times: 0.9%Begging the Question2.4%This article: 19.0%Fabiola Zerpa: 4.7%The Japan Times: 4.2%Post Hoc (False Cause)19.0%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.1%Tu Quoque0.0%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.5%Burden of Proof0.0%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.1%Appeal to Nature0.0%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.2%Composition/Division0.0%This article: 4.1%Fabiola Zerpa: 1.0%The Japan Times: 1.0%Anecdotal4.1%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.0%No True Scotsman0.0%This article: 3.1%Fabiola Zerpa: 0.8%The Japan Times: 3.0%Ambiguity (Equivocation)3.1%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.1%Middle Ground0.0%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.0%Personal Incredulity0.0%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.1%Special Pleading0.0%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.0%Genetic Fallacy0.0%This article: 15.2%Fabiola Zerpa: 3.8%The Japan Times: 4.5%Unattributed Quote15.2%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 1.8%Quote-first Misdirection0.0%This article: 25.5%Fabiola Zerpa: 6.4%The Japan Times: 9.0%Biased Writer Voice25.5%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 1.6%Indoctrination0.0%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.6%Politically Left Leaning Bias0.0%This article: 0.0%Fabiola Zerpa: 0.0%The Japan Times: 0.2%Politically Right Leaning Bias0.0%This article: 6.6%Fabiola Zerpa: 2.6%The Japan Times: 0.5%Attempt to Sell a Product or S…6.6%

290 words analyzed.

Speakers

5speakers36%attributed speech187writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 12 words • 100.0% coverageWriter's voice • 8 words • 100.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 17 words • 100.0% coverageCarlos Genatios • 31 words • 100.0% coverageWriter's voice • 7 words • 100.0% coverageCarlos Genatios • 34 words • 0.0% coverageWriter's voice • 9 words • 100.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 3 words • 0.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 12 words • 100.0% coverageWriter's voice • 13 words • 100.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageSompo • 8 words • 100.0% coverageJT Media Enterprise Division • 10 words • 100.0% coverageWriter's voice • 1 words • 100.0% coverageWriter's voice • 11 words • 0.0% coverageLindsey Graham • 9 words • 100.0% coverageSouth Korea • 11 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverage
Selected voice

Lindsey Graham

100%flagged-word coverage
9 attributed words8.7% of attributed speech97% writer coverage
0%50.0%100.0%Biased Writer Voice+65.2 ptsWriter: 34.8%Lindsey Graham: 100.0%100.0%Unattributed Quote-7.0 ptsWriter: 7.0%Lindsey Graham: 0.0%0.0%Attempt to Sell a Product -0.5 ptsWriter: 0.5%Lindsey Graham: 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.