Raw Story⁠93%

Dem senator confronts Mike Waltz over inaccurate war death toll ⁠72%

By María Teresita Armstrong-Matta⁠92%

7/23/2026, 12:45:01 AM

BS Summary: This article contains 15 faulty reasoning types, including Confirmation Bias, Appeal to Emotion, and Ambiguity (Equivocation), with Negativity Bias as the most egregious example at 35.5% saturation with 70 hits. Analysis detected 354 faulty-reasoning hits from 197 analyzed words, generating a BS Score of 64.5% and a BS Rank of ⁠72% (5,986 of 20,931 articles). This article is worse (more manipulative) than 71.40% of the article peer group.

Rep. 
Ted Lieu (D-CA) engaged in a heated confrontation with U.S. 
Ambassador Mike Waltz. 
The altercation occurred during a House Committee on Foreign Affairs hearing Wednesday over casualty figures from the Iran war, according to KCRA. 
When Waltz claimed only "over a hundred" U.S. troops had been wounded, Lieu corrected him sharply, "That number is wrong." 
Lieu then cited the Pentagon's own Defense Casualty Analysis System showing 482 wounded. 
A total of 18 U.S. service members have been reported killed, including one whose death the Pentagon has not formally confirmed. 
Lieu challenged the contradiction between President Donald Trump's March claim that Iran's military was "massively obliterated" and the mounting casualty toll. 
"You should be ashamed! 
You don't know basic facts!" 
Claimed Lieu. 
Waltz defended the damage assessment, claiming Iranian military capability was "massively damaged, if not completely ineffective." 
"You should resign! 
You don't even care about the number of U.S. troops that were wounded!" 
Lieu said. 
Another Republican colleague intervened and pushed to strike Lieu's words from the record. 
"He lied to Congress!" 
Lieu said, "November is coming for you and this administration!" 
Watch the video below. 
Article reasoning-pattern comparisonThis article: 20.8%María Teresita Armstrong-Matta: 10.0%Rawstory: 7.7%Confirmation Bias20.8%This article: 0.0%María Teresita Armstrong-Matta: 3.4%Rawstory: 0.8%Anchoring Bias0.0%This article: 10.7%María Teresita Armstrong-Matta: 3.8%Rawstory: 4.4%Availability Heuristic10.7%This article: 0.0%María Teresita Armstrong-Matta: 1.0%Rawstory: 1.1%Representativeness Heuristic0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.7%Rawstory: 1.0%Hindsight Bias0.0%This article: 0.0%María Teresita Armstrong-Matta: 6.2%Rawstory: 2.2%Overconfidence Bias0.0%This article: 10.7%María Teresita Armstrong-Matta: 16.2%Rawstory: 13.7%Framing Effect10.7%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 0.4%Loss Aversion0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.3%Rawstory: 0.4%Status Quo Bias0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 0.1%Sunk Cost Effect0.0%This article: 8.1%María Teresita Armstrong-Matta: 0.7%Rawstory: 0.7%Optimism Bias8.1%This article: 0.0%María Teresita Armstrong-Matta: 2.3%Rawstory: 2.8%Pessimism Bias0.0%This article: 35.5%María Teresita Armstrong-Matta: 18.6%Rawstory: 21.8%Negativity Bias35.5%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 1.1%Self-Serving Bias0.0%This article: 0.0%María Teresita Armstrong-Matta: 8.0%Rawstory: 2.9%Fundamental Attribution Error0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 0.3%Actor-Observer Bias0.0%This article: 0.0%María Teresita Armstrong-Matta: 1.8%Rawstory: 2.5%In-Group Bias0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.6%Rawstory: 1.6%Out-Group Homogeneity Bias0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 0.6%Halo Effect0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.5%Rawstory: 0.9%Horn Effect0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 0.0%Dunning-Kruger Effect0.0%This article: 5.1%María Teresita Armstrong-Matta: 0.5%Rawstory: 1.7%Recency Bias5.1%This article: 0.0%María Teresita Armstrong-Matta: 0.3%Rawstory: 0.7%Primacy Effect0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 0.1%Blind-Spot Bias0.0%This article: 11.2%María Teresita Armstrong-Matta: 11.5%Rawstory: 6.4%Ad Hominem11.2%This article: 0.0%María Teresita Armstrong-Matta: 0.7%Rawstory: 0.7%Straw Man0.0%This article: 6.6%María Teresita Armstrong-Matta: 0.8%Rawstory: 3.8%Appeal to Authority6.6%This article: 0.0%María Teresita Armstrong-Matta: 3.6%Rawstory: 2.9%False Dilemma0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.3%Rawstory: 1.2%Slippery Slope0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 0.3%Circular Reasoning0.0%This article: 0.0%María Teresita Armstrong-Matta: 7.2%Rawstory: 11.9%Hasty Generalization0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.6%Rawstory: 0.7%Red Herring0.0%This article: 0.0%María Teresita Armstrong-Matta: 1.4%Rawstory: 0.8%Bandwagon0.0%This article: 19.8%María Teresita Armstrong-Matta: 6.7%Rawstory: 9.2%Appeal to Emotion19.8%This article: 10.7%María Teresita Armstrong-Matta: 3.2%Rawstory: 1.6%Begging the Question10.7%This article: 5.1%María Teresita Armstrong-Matta: 0.9%Rawstory: 3.6%Post Hoc (False Cause)5.1%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 0.6%Tu Quoque0.0%This article: 2.0%María Teresita Armstrong-Matta: 3.4%Rawstory: 1.5%Burden of Proof2.0%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 0.1%Appeal to Nature0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.7%Rawstory: 0.4%Composition/Division0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.6%Rawstory: 3.9%Anecdotal0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 0.3%No True Scotsman0.0%This article: 18.3%María Teresita Armstrong-Matta: 2.8%Rawstory: 2.4%Ambiguity (Equivocation)18.3%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 0.0%Gambler’s Fallacy0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 0.1%Middle Ground0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 0.2%Personal Incredulity0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 0.1%Special Pleading0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 0.4%Genetic Fallacy0.0%This article: 10.2%María Teresita Armstrong-Matta: 4.4%Rawstory: 5.4%Unattributed Quote10.2%This article: 0.0%María Teresita Armstrong-Matta: 4.2%Rawstory: 4.2%Quote-first Misdirection0.0%This article: 5.1%María Teresita Armstrong-Matta: 9.9%Rawstory: 15.6%Biased Writer Voice5.1%This article: 0.0%María Teresita Armstrong-Matta: 3.7%Rawstory: 3.2%Indoctrination0.0%This article: 0.0%María Teresita Armstrong-Matta: 6.7%Rawstory: 8.0%Politically Left Leaning Bias0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 0.9%Politically Right Leaning Bias0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.6%Rawstory: 0.5%Attempt to Sell a Product or S…0.0%

197 words analyzed.

Speakers

4speakers51%attributed speech96writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 10 words • 100.0% coverageWriter's voice • 1 words • 0.0% coverageTed Lieu • 10 words • 0.0% coverageAmbassador Mike Waltz • 3 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageTed Lieu • 20 words • 0.0% coveragePentagon's Defense Casualty Analysis System • 13 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageTed Lieu • 4 words • 0.0% coverageTed Lieu • 5 words • 0.0% coverageTed Lieu • 2 words • 0.0% coverageMike Waltz • 16 words • 100.0% coverageMike Waltz • 3 words • 0.0% coverageTed Lieu • 13 words • 0.0% coverageTed Lieu • 2 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 4 words • 100.0% coverageTed Lieu • 10 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverage
Selected voice

Mike Waltz

100%flagged-word coverage
19 attributed words19% of attributed speech58% writer coverage
0%42.5%85.0%Unattributed Quote+80.0 ptsWriter: 4.2%Mike Waltz: 84.2%84.2%Biased Writer Voice-10.4 ptsWriter: 10.4%Mike Waltz: 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.