Media Manipulation and Bias Detection
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HonestyMeter - AI powered bias detection
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Internet 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.
The use of sensational language to evoke strong reactions.
The headline and content suggest that the video 'leaves internet divided' and 'disgust the audience' which is sensational and may not represent the full spectrum of reactions.
Use a neutral headline and avoid suggesting that the audience is universally disgusted.
Language that is partial or prejudiced towards one side.
Terms like 'disgust the audience' and 'chappal maro isko' (hit her with a slipper) are biased and derogatory.
Remove derogatory language and present the event in a neutral manner.
Attempting to manipulate an emotional response in place of a valid or compelling argument.
The article appeals to emotions by highlighting negative comments and reactions without providing a balanced view.
Include a range of reactions, not just negative ones, to provide a balanced perspective.
- 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.