Media Manipulation and Bias Detection
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HonestyMeter - AI powered bias detection
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Khloé Kardashian
Caution! Due to inherent human biases, it may seem that reports on articles aligning with our views are crafted by opponents. Conversely, reports about articles that contradict our beliefs might seem to be authored by allies. However, such perceptions are likely to be incorrect. These impressions can be caused by the fact that in both scenarios, articles are subjected to critical evaluation. This report is the product of an AI model that is significantly less biased than human analyses and has been explicitly instructed to strictly maintain 100% neutrality.
Nevertheless, HonestyMeter is in the experimental stage and is continuously improving through user feedback. If the report seems inaccurate, we encourage you to submit feedback , helping us enhance the accuracy and reliability of HonestyMeter and contributing to media transparency.
Use of exciting or shocking stories at the expense of accuracy, to provoke public interest or excitement.
The title 'Khloé Kardashian's gesture of LOVE with Kanye West' uses sensationalism to attract readers.
Use a more neutral title that accurately reflects the content of the article.
Language that is biased or contains value judgments.
The use of phrases like 'rocky past', 'tense relationship', and 'broke down in tears' introduces bias by emphasizing the negative aspects of the relationships.
Use neutral language to describe the relationships without implying judgment.
Attempting to manipulate an emotional response in place of a valid or compelling argument.
The article appeals to emotion by detailing the personal struggles and emotional responses of the individuals involved, such as 'broke down in tears' and 'It’s really f–king hard.'
Focus on factual reporting without emphasizing emotional responses.
- This is an EXPERIMENTAL DEMO version that is not intended to be used for any other purpose than to showcase the technology's potential. We are in the process of developing more sophisticated algorithms to significantly enhance the reliability and consistency of evaluations. Nevertheless, even in its current state, HonestyMeter frequently offers valuable insights that are challenging for humans to detect.