
Lessons for the resistance 2.0 on how to fight back against Trump | Jan-Werner Mueller
- Bias Rating
- Reliability
60% ReliableAverage
- Policy Leaning
10% Center
- Politician Portrayal
-50% Negative
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Bias Score Analysis
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
-4% Negative
- Conservative
Sentence | Sentiment | Bias |
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Reliability Score Analysis
Policy Leaning Analysis
Politician Portrayal Analysis
Bias Meter
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Contributing sentiments towards policy:
41% : True, it had genuine shock value: everyone knew that Trump was an aspiring authoritarian devoted only to dollars and domination (and, more specifically, the ideology of white supremacy); yet almost half of those who bothered to go to the polls still voted for him.33% : Of course, these were not solely responsible for preventing the plutocratic power grab we are witnessing now: back then, Trump had neither a plan nor the personnel he has today.
31% : Surveys show that majorities are unhappy with Trump and Musk (but also with Democratic leadership).
30% : Instead, Democrats should be true to their own message from the campaign, for it has been vindicated: Trump is proving an existential threat to democracy.
28% : But we cannot give up A month into Trump 2.0, pushback from Democrats and civil society remains surprisingly weak.
16% : But the casual dismissal of opposition under Trump 1.0 is an unforced error.
*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.