Media Manipulation and Bias Detection
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HonestyMeter - AI powered bias detection
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Sabrina Ionescu Supporters
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 hyperbolic language to create buzz.
The phrase 'controversy has made Smith the talk of the town' is sensationalist as it exaggerates the significance of the event.
Replace with a more neutral description, such as 'Smith's comments have sparked discussion among fans and media.'
Language that is partial or shows a lack of neutrality.
The use of 'boldly asserted' and 'clap back' suggests a bias against Smith and in favor of Miller and Ionescu.
Use neutral language such as 'Smith suggested' and 'Miller responded.'
Attempting to manipulate an emotional response in place of a valid or compelling argument.
The article uses emotionally charged language like 'horrific commentating' and 'embarrassing reverse slam blunder' to elicit a negative response towards Smith.
Remove emotionally charged language and present the facts without bias.
- 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.