There's a New Liberal Politician Trump Is Blaming for California Fires
- Bias Rating
94% Very Conservative
- Reliability
50% ReliableFair
- Policy Leaning
94% Very Conservative
- Politician Portrayal
-62% Negative
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The A.I. bias rating includes policy and politician portrayal leanings based on the author’s tone found in the article using machine learning. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral.
Sentiments
-51% Negative
- Liberal
- Conservative
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Reliability Score Analysis
Policy Leaning Analysis
Politician Portrayal Analysis
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Contributing sentiments towards policy:
71% : "Such advice rings similar to the advice that Trump gave to Newsom in November 2019, during his first term as president.52% : Later still on Thursday, Trump was out with yet another post, this one focussing more on Newsom.
38% : "Governor Gavin Newscum should immediately go to Northern California and open up the water main, and let the water flow into his dry, starving, burning State, instead of having it go out into the Pacific Ocean," Trump made clear.
38% : "It's ashes, and Gavin Newscum should resign," Trump posted about California, referring to it as "[o]ne of the best and most beautiful parts of the United States of America," also using that same insult for the governor.
22% : "Fire is spreading rapidly for 3 days -- ZERO CONTAINMENT," Trump lamented early on Thursday morning.
19% : Late on Wednesday night, also over Truth Social, Trump called on Newsom to resign.
17% : Just as he did in a post from Wednesday, Trump also lashed out against "Biden's FEMA" and a lack of funds.
11% : Trump has blamed Democratic Gov. Gavin Newsom, as well as highlighted problems with a lack of water for the fire hydrants.
9% : In this case, Trump blamed it on how the FEMA funding was "all wasted on the Green New Scam!"As Matt's been covering, Bass wasn't even in Los Angeles.
*Our bias meter rating uses data science including sentiment analysis, machine learning and our proprietary algorithm for determining biases in news articles. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral. The rating is an independent analysis and is not affiliated nor sponsored by the news source or any other organization.