FLASHBACK: Trump has long history of warning Newsom over 'terrible' wildfire prevention
- Bias Rating
58% Medium Conservative
- Reliability
45% ReliableFair
- Policy Leaning
64% Medium Conservative
- Politician Portrayal
-31% 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
-27% Negative
- Liberal
- Conservative
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Policy Leaning Analysis
Politician Portrayal Analysis
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Contributing sentiments towards policy:
56% : Just roughly two weeks before Trump will be inaugurated as the nation's 47th president, he again took aim at Newsom's wildfire prevention leadership in the state, pinning blame for the LA County fires on Newsom and his environmental policies.36% : HEIDI MONTAG, SPENCER PRATT LOSE HOME; CELEBRITIES FLEE RITZY NEIGHBORHOODTrump has a long history of putting Newsom's handling of wildfires under the microscope across his first four years in the White House, including in January 2019 when he threatened to cut off federal funds to California if reforms were not made to the state's forest management services.
31% : CALIFORNIA WILDFIRES RAGE ACROSS LOS ANGELES COUNTY, FORCING THOUSANDS TO EVACUATE THEIR HOMES"We're successfully waging war against thousands of fires started across the state in the last few weeks due to extreme weather created by climate change while Trump is conducting a full on assault against the antidotes," Newsom said in response to Trump's message, the Washington Post reported at the time.
26% : You are excused from this conversation," Newsom shot back at Trump in 2019, for example, after the president slammed him for his wildfire leadership.
22% : California Republicans, however, have echoed Trump that Newsom and other Democrats in the state "failed" in addressing forestry management.
*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.