Media Manipulation and Bias Detection
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HonestyMeter - AI powered bias detection
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Elvish Yadav
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 opens with a sensational tone by mentioning 'controversies due to various reasons ranging from snake poison cases to assaults.'
Provide context for the controversies mentioned to avoid sensationalism.
Use of language that is not neutral and shows author's preferences.
The use of the word 'childish' to describe Elvish's reaction and 'arrogant' to describe Elvish himself introduces biased language.
Use neutral language to describe the actions and characteristics of the individuals involved.
Attempting to manipulate an emotional response in place of a valid or compelling argument.
The article appeals to emotion by focusing on the personal feelings of hurt expressed by Manisha Rani, which may sway the reader's sympathy.
Stick to the facts of the situation rather than emphasizing the emotional aspects.
- 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.