
A year of hate: what I learned when I went undercover with the far right
- Bias Rating
8% Center
- Reliability
65% ReliableAverage
- 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
15% Positive
- Liberal
- 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:
59% : Before long, I would turn my head if I heard a shout of "Chris!"57% : I had been experimenting with undercover reporting on my own - at a house of anti-Satanic conspiracy theorists in Birmingham, for instance, and a sect of antisemitic Catholics in Southport.
56% : During the pandemic, I was a journalist reporting on how far-right activists were campaigning with anti-vaccine conspiracists.
55% : He shook my hand and said his name was Matt Archer - a pseudonym, I later learned.
55% : It was a bougie place, in keeping with my image as a wealthy investor - the menu had potted partridge and £350 burgundies.
54% : As Chris, I had to repeat the name in my mind whenever I was walking into a meeting in order to remind myself who I was pretending to be.
46% : Best of all for the guests, he had booked Jared Taylor, a well-known advocate of segregation who has written: "Blacks and whites are different.
45% : I rejoined using my Chris account, my face burning, ready with a lie about how I had borrowed a work laptop.
29% : I wore the drabbest clothes in my wardrobe: a white shirt, a zip-up fleece, a pair of navy trousers, a sad anorak and a fake lanyard around my neck.
23% : They have been energised by the re-election in the US of Donald Trump.
*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.