Media Manipulation and Bias Detection
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HonestyMeter - AI powered bias detection
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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.
Exaggerating or sensationalizing aspects of a story to attract attention.
Phrases like 'sad final performance' and 'wheeled out like that' contribute to a sensational tone, potentially exaggerating the situation.
Use more neutral language to describe the performance, such as 'Ozzy Osbourne's final performance with Black Sabbath was met with mixed reactions from fans.'
Using emotionally charged language to influence readers' feelings.
Statements like 'I feel bad for Ozzy' and 'it's kinda gross how a visibly unwell man is being wheeled out' are designed to evoke sympathy and concern.
Present the facts about Ozzy's health and performance without emotionally charged language, allowing readers to form their own opinions.
- 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.