There's one lesson the Trudeau Liberals must learn from Trump's victory
- Bias Rating
10% Center
- Reliability
75% ReliableGood
- Policy Leaning
10% Center
- Politician Portrayal
-30% 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
-7% Negative
- Liberal
- Conservative
Sentence | Sentiment | Bias |
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Reliability Score Analysis
Policy Leaning Analysis
Politician Portrayal Analysis
Bias Meter
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-100%
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100%
Conservative
Contributing sentiments towards policy:
58% : (Rogan's endorsement went to Trump this year.)56% : Trump mimicked the anger of working people, offered them a return to optimism, and reached them on the channels they listen to.
50% : Many, myself included, thought voters would see Trump for the charlatan that he is.
38% : They avoided trying to break into, or compete with, the toxic information environment in which Trump lives.
25% : The only tactic left at their disposal is to compare Poilievre to Trump, unconvincingly and to the frustration of the Conservatives' new-found working-class base.
18% : ARTICLE CONTINUES BELOW ARTICLE CONTINUES BELOWIn trying to paint Trump as a danger to democracy, the Democrats twisted themselves in knots to become guardians of the status quo.
15% : The post-mortem of Harris's stunning loss is still ongoing, but it's incontrovertible that Trump is returning to the White House because he won over the working class, particularly men.
15% : Harris spent the campaign bombarding voters with tightly-messaged scripts about why they should vote against Trump.
10% : Not long after Harris called Trump a "fascist," Liberal MP Adam van Koeverden claimed that Conservative leader Pierre Poilievre "poses an existential threat to Canada's democracy."
*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.