Media Manipulation and Bias Detection
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HonestyMeter - AI powered bias detection
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Social Media Users
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 article focuses on a photo of NBA YoungBoy holding hands with a fan, emphasizing the reactions and controversy, which may exaggerate the significance of the event.
Provide context for why the photo is newsworthy without emphasizing the controversy.
Use of language that is not neutral and shows author's prejudices.
Terms like 'lucky listener', 'under fire', and 'crying on the net' show a lack of neutrality in describing the fan and the reactions.
Use neutral language to describe the fan's experience and the reactions from others.
Claims made without evidence to support them.
The article includes statements like 'insecure bums who [get] no money' without providing evidence to support the characterizations of the individuals involved.
Avoid including inflammatory statements without evidence, or provide a clear source for such claims.
- 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.