Raw Story⁠93%

Trump urges communities to ignore data center complaints: 'Can't fight it' ⁠67%

By María Teresita Armstrong-Matta⁠90%

7/24/2026, 12:45:01 AM

BS Summary: This article contains 17 faulty reasoning types, including Negativity Bias, Availability Heuristic, and Representativeness Heuristic, with Indoctrination as the most egregious example at 33.3% saturation with 52 hits. Analysis detected 333 faulty-reasoning hits from 156 analyzed words, generating a BS Score of 60.7% and a BS Rank of ⁠67% (7,270 of 21,886 articles). This article is worse (more manipulative) than 66.80% of the article peer group.

President Donald Trump urged tech companies Thursday to convince local communities to accept AI data centers. 
"You have to convince your communities how great these things are. 
You can't fight it. 
You have to go with it," the President said. 
Trump made the remarks during a speech unveiling his "Ratepayer Protection Pledge" at the Environmental Protection Agency. 
The Trump administration is fast-tracking data center construction past environmental rules, but the projects have become politically contentious. 
Polling shows overwhelming majorities of voters across partisan lines oppose data center construction near their communities, Gallup found, citing concerns about local resource waste and environmental impacts. 
Some view data centers as a proxy for broader AI technology expansion. 
The issue has created deep fractures in Republican-dominated states like Florida, where party members disagree sharply over development policy, demonstrating the political complexity surrounding data center deployment. 
Watch the video below. 
Article reasoning-pattern comparisonThis article: 0.0%María Teresita Armstrong-Matta: 9.8%Rawstory: 8.4%Confirmation Bias0.0%This article: 0.0%María Teresita Armstrong-Matta: 3.6%Rawstory: 0.8%Anchoring Bias0.0%This article: 17.3%María Teresita Armstrong-Matta: 4.4%Rawstory: 4.5%Availability Heuristic17.3%This article: 17.3%María Teresita Armstrong-Matta: 0.9%Rawstory: 1.1%Representativeness Heuristic17.3%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: 14.1%María Teresita Armstrong-Matta: 15.6%Rawstory: 13.4%Framing Effect14.1%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 0.4%Loss Aversion0.0%This article: 8.3%María Teresita Armstrong-Matta: 0.3%Rawstory: 0.4%Status Quo Bias8.3%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 0.1%Sunk Cost Effect0.0%This article: 7.1%María Teresita Armstrong-Matta: 0.6%Rawstory: 0.7%Optimism Bias7.1%This article: 2.6%María Teresita Armstrong-Matta: 2.3%Rawstory: 2.9%Pessimism Bias2.6%This article: 28.8%María Teresita Armstrong-Matta: 20.7%Rawstory: 21.6%Negativity Bias28.8%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 1.2%Self-Serving Bias0.0%This article: 7.7%María Teresita Armstrong-Matta: 7.0%Rawstory: 2.8%Fundamental Attribution Error7.7%This article: 0.0%María Teresita Armstrong-Matta: 0.0%Rawstory: 0.3%Actor-Observer Bias0.0%This article: 17.3%María Teresita Armstrong-Matta: 1.6%Rawstory: 2.5%In-Group Bias17.3%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: 2.6%María Teresita Armstrong-Matta: 3.8%Rawstory: 3.0%False Dilemma2.6%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: 17.3%María Teresita Armstrong-Matta: 1.2%Rawstory: 0.8%Bandwagon17.3%This article: 0.0%María Teresita Armstrong-Matta: 6.7%Rawstory: 9.2%Appeal to Emotion0.0%This article: 5.8%María Teresita Armstrong-Matta: 2.8%Rawstory: 1.6%Begging the Question5.8%This article: 0.0%María Teresita Armstrong-Matta: 0.8%Rawstory: 3.8%Post Hoc (False Cause)0.0%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: 17.3%María Teresita Armstrong-Matta: 0.6%Rawstory: 0.5%Composition/Division17.3%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: 0.0%María Teresita Armstrong-Matta: 3.1%Rawstory: 2.4%Ambiguity (Equivocation)0.0%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: 7.1%María Teresita Armstrong-Matta: 4.2%Rawstory: 5.3%Unattributed Quote7.1%This article: 7.1%María Teresita Armstrong-Matta: 4.6%Rawstory: 4.1%Quote-first Misdirection7.1%This article: 0.0%María Teresita Armstrong-Matta: 9.1%Rawstory: 15.7%Biased Writer Voice0.0%This article: 33.3%María Teresita Armstrong-Matta: 3.3%Rawstory: 3.2%Indoctrination33.3%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: 2.6%María Teresita Armstrong-Matta: 0.5%Rawstory: 0.5%Attempt to Sell a Product or S…2.6%

156 words analyzed.

Speakers

2speakers43%attributed speech89writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 11 words • 100.0% coveragePresident Donald Trump • 16 words • 100.0% coveragePresident Donald Trump • 11 words • 100.0% coveragePresident Donald Trump • 4 words • 0.0% coveragePresident Donald Trump • 9 words • 100.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageGallup • 27 words • 100.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 4 words • 100.0% coverage
Selected voice

Gallup

100%flagged-word coverage
27 attributed words40% of attributed speech81% writer coverage
0%50.0%100.0%Indoctrination+100.0 ptsWriter: 0.0%Gallup: 100.0%100.0%Quote-first Misdirection-12.4 ptsWriter: 12.4%Gallup: 0.0%0.0%Attempt to Sell a Product -4.5 ptsWriter: 4.5%Gallup: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

Loading…
Loading…
Loading…

Analysis

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