Democrats Target Clarence Thomas in Push for SCOTUS Recusal Standards

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Published Apr 29, 2022
Reliability
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Biasly's Reliability Score provides a rating of the quality of sources and quotes used in the article including their uniqueness and diversity. Grades are computed on a scale from A to F with A being an Excellent Reliability.
N/A
Bias Rating
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The Bias Rating is computed based on a number of factors including bias loaded words, sentiments towards certain political policies, author bias towards politicians, and the amount of tone found in the article.

26% Moderately Conservative

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*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.

Policy Leaning
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The policy leaning score is derived from author biases for or against a certain political policy, as found in articles.

-2% Center

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*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.

Politician Portrayal
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The politician leaning score is determined by the author's tone and leaning towards the specific politician mentioned in the article.

80% Negative

Opposite Viewpoint
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Reliability Score Analysis

The Reliability Score of the article is determined on a percentage score basis from 0 to 100%.

  • Opposite Sources as Poor for the lower number of sources with different viewpoints.
  • Unique Sources as Poor for the lower number of different sources.
  • Multiple Sources as Poor for the lower number of total sources.
  • Multiple Quotes as Poor for the lower number of quotes used in the article.
  • Quote Length as Poor for the lower number of words used in each quote.

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.


Policy Leaning Analysis

This article includes the following sentiments, providing an average bias score of -2% Liberal:

  • 1 positive sentiment for Affirmative Action
  • 1 positive sentiment for Tuition-Free College


Politician Portrayal Analysis:

This article includes the following Politician Portrayal sentiments, providing an average sentiment of 80% Negative and bias score of 80% Conservative:

2 negative sentiments and 2 positive sentiments for Hakeem Jeffries
4 negative sentiments and 2 positive sentiments for Bennie G. Thompson


Policies:

Affirmative Action
Tuition-Free College

Politicians:

Hakeem Jeffries
Bennie G. Thompson

Sentiments

  •   Liberal
2% "nnAfter Jeffries asked what evidence Paoletta had to support his "incendiary charge," Paoletta pointed out that Thompson called Justice Thomas an "Uncle Tom because ..."
2% "nnAfter Jeffries asked what evidence Paoletta had to support his "incendiary charge," Paoletta pointed out that Thompson called Justice Thomas an "Uncle Tom because ..."
4% "After Jeffries asked what evidence Paoletta had to support his incendiary charge, Paoletta pointed out that Thompson called Justice Thomas an Uncle Tom because ..."
2% "nnAfter Jeffries asked what evidence Paoletta had to support his "incendiary charge," Paoletta pointed out that Thompson called Justice Thomas an "Uncle Tom because ..."
2% "nnAfter Jeffries asked what evidence Paoletta had to support his "incendiary charge," Paoletta pointed out that Thompson called Justice Thomas an "Uncle Tom because ..."
-2% The notion that that is, when some members on this side of the aisle and others have been called the N-word throughout different points ..."
-16% Appearing to set up the premise that Democrats are not simply trying to attack Justice Thomas, committee member Rep. Hakeem Jeffries (D-NY) addresses Paoletta, ..."
-20% When Paoletta asked Jeffries if he could give him another example of Democrat hatred for Justice Thomas, the New York Democrat simply replied, "No."nnPaoletta's ..."
-24% Indeed, the "House Select Committee to Investigate the January 6th Attack on the United States Capitol" -- led by Rep. Bennie Thompson (D-MS) -- ..."
-24% "It's almost to the point saying this man doesn't like black people, he doesn't like being black," Thompson said in addition."

We have listed the top 10 sentiments. More sentiments do exist. Please review the full article for more information.


*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. The rating is an independent analysis and is not affiliated nor sponsored by the news source or any other organization.

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Contributing sentiments towards policy:

52% : "After Jeffries asked what evidence Paoletta had to support his "incendiary charge," Paoletta pointed out that Thompson called Justice Thomas an "Uncle Tom because of his views on voter ID and affirmative action, when in fact more black Americans support voter ID, and with respect to affirmative action in college education, 62 percent are opposed to it.""So that," he continued, "is the most vile, disgusting thing you can say."Cutting off Paoletta, Jeffries appeared to defend Thompson by setting up two red herrings, saying "there are a lot of vile, disgusting things that can be said. ...

*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.

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