Raw Story⁠93%

Mike Johnson seeks to appease MAGA with a controversial bill - Raw Story ⁠70%

By María Teresita Armstrong-Matta⁠90%

7/8/2026, 5:35:01 PM

BS Summary: This article contains 11 faulty reasoning types, including Negativity Bias, Framing Effect, and Unattributed Quote, with Biased Writer Voice as the most egregious example at 30.1% saturation with 63 hits. Analysis detected 340 faulty-reasoning hits from 209 analyzed words, generating a BS Score of 62.7% and a BS Rank of ⁠70% (6,703 of 21,887 articles). This article is worse (more manipulative) than 69.40% of the article peer group.

U.S. 
House Speaker Mike Johnson (R-LA) speaks to the media as he arrives for a House Republican Caucus meeting at the U.S. 
Capitol in Washington, D.C., U.S., June 30, 2026. 
REUTERS/Kylie Cooper 
House Speaker Mike Johnson (R-LA) is preparing to introduce controversial legislation banning "birth tourism", or when a pregnant woman enters the U.S. to secure citizenship for their children. 
According to Politico , Johnson plans to introduce the bill to mollify far-right caucus members he angered. 
The House Speaker had pledged to hold a vote before July 4 on legislation codifying President Donald Trump's border security priorities in exchange for hard-liners' support on a narrower immigration funding bill. 
When that vote never occurred, rebellious GOP members halted legislative business, forcing leadership to adjourn early for the holiday recess, Politico explains. 
Political analysts view the timing as evidence of Johnson's desperation to repair fractured relationships within his divided caucus. 
"House passage of a measure to crack down the practice would be largely symbolic, as it stands no chance of overcoming the Senate filibuster," Politico reports. 
The Republican caucus remains deeply fractured on immigration policy. 
Watch the video below. 
Your browser does not support the video tag. 
Article reasoning-pattern comparisonThis article: 8.6%María Teresita Armstrong-Matta: 9.8%Rawstory: 8.4%Confirmation Bias8.6%This article: 0.0%María Teresita Armstrong-Matta: 3.6%Rawstory: 0.8%Anchoring Bias0.0%This article: 0.0%María Teresita Armstrong-Matta: 4.4%Rawstory: 4.5%Availability Heuristic0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.9%Rawstory: 1.1%Representativeness Heuristic0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.6%Rawstory: 1.0%Hindsight Bias0.0%This article: 0.0%María Teresita Armstrong-Matta: 5.8%Rawstory: 2.2%Overconfidence Bias0.0%This article: 27.8%María Teresita Armstrong-Matta: 15.6%Rawstory: 13.4%Framing Effect27.8%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: 0.0%María Teresita Armstrong-Matta: 0.6%Rawstory: 0.7%Optimism Bias0.0%This article: 0.0%María Teresita Armstrong-Matta: 2.3%Rawstory: 2.9%Pessimism Bias0.0%This article: 29.7%María Teresita Armstrong-Matta: 20.7%Rawstory: 21.6%Negativity Bias29.7%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 1.2%Self-Serving Bias0.0%This article: 8.1%María Teresita Armstrong-Matta: 7.0%Rawstory: 2.8%Fundamental Attribution Error8.1%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: 1.6%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: 0.7%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: 0.0%María Teresita Armstrong-Matta: 1.1%Rawstory: 1.8%Recency Bias0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.4%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: 0.0%María Teresita Armstrong-Matta: 10.2%Rawstory: 6.3%Ad Hominem0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.6%Rawstory: 0.7%Straw Man0.0%This article: 0.0%María Teresita Armstrong-Matta: 3.3%Rawstory: 4.1%Appeal to Authority0.0%This article: 0.0%María Teresita Armstrong-Matta: 3.8%Rawstory: 3.0%False Dilemma0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.3%Rawstory: 1.5%Slippery Slope0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 0.2%Circular Reasoning0.0%This article: 0.0%María Teresita Armstrong-Matta: 7.7%Rawstory: 12.1%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: 1.2%Rawstory: 0.8%Bandwagon0.0%This article: 8.1%María Teresita Armstrong-Matta: 6.7%Rawstory: 9.2%Appeal to Emotion8.1%This article: 0.0%María Teresita Armstrong-Matta: 2.8%Rawstory: 1.6%Begging the Question0.0%This article: 10.5%María Teresita Armstrong-Matta: 0.8%Rawstory: 3.8%Post Hoc (False Cause)10.5%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 0.6%Tu Quoque0.0%This article: 0.0%María Teresita Armstrong-Matta: 3.7%Rawstory: 1.7%Burden of Proof0.0%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: 0.6%Rawstory: 0.5%Composition/Division0.0%This article: 0.0%María Teresita Armstrong-Matta: 1.2%Rawstory: 3.9%Anecdotal0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 0.3%No True Scotsman0.0%This article: 8.6%María Teresita Armstrong-Matta: 3.1%Rawstory: 2.4%Ambiguity (Equivocation)8.6%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: 20.6%María Teresita Armstrong-Matta: 4.2%Rawstory: 5.3%Unattributed Quote20.6%This article: 0.0%María Teresita Armstrong-Matta: 4.6%Rawstory: 4.1%Quote-first Misdirection0.0%This article: 30.1%María Teresita Armstrong-Matta: 9.1%Rawstory: 15.7%Biased Writer Voice30.1%This article: 8.6%María Teresita Armstrong-Matta: 3.3%Rawstory: 3.2%Indoctrination8.6%This article: 0.0%María Teresita Armstrong-Matta: 6.2%Rawstory: 8.0%Politically Left Leaning Bias0.0%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 1.0%Politically Right Leaning Bias0.0%This article: 1.9%María Teresita Armstrong-Matta: 0.5%Rawstory: 0.5%Attempt to Sell a Product or S…1.9%

209 words analyzed.

Speakers

2speakers32%attributed speech142writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 13 words • 100.0% coverageWriter's voice • 1 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageREUTERS • 2 words • 0.0% coverageWriter's voice • 28 words • 100.0% coveragePolitico • 17 words • 100.0% coverageWriter's voice • 32 words • 0.0% coveragePolitico • 22 words • 100.0% coverageWriter's voice • 18 words • 100.0% coveragePolitico • 26 words • 100.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 4 words • 100.0% coverageWriter's voice • 8 words • 0.0% coverage
Selected voice

Politico

100%flagged-word coverage
65 attributed words97% of attributed speech51% writer coverage
0%35.0%70.0%Unattributed Quote+66.2 ptsWriter: 0.0%Politico: 66.2%66.2%Biased Writer Voice+5.0 ptsWriter: 28.9%Politico: 33.8%33.8%Indoctrination-12.7 ptsWriter: 12.7%Politico: 0.0%0.0%Attempt to Sell a Product -2.8 ptsWriter: 2.8%Politico: 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.