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.
Use of sensational language to provoke interest at the expense of accuracy.
The title 'Shannon Sharpe and Kendrick Perkins argue like schoolgirls over who's a bigger LeBron James fan' uses sensational language that trivializes the discussion.
Use a more neutral title that accurately reflects the content of the discussion.
Language that is partial or prejudiced towards particular views.
Phrases like 'Shannon Sharpe and Kendrick Perkins are in love with LeBron' and 'argue like schoolgirls' are biased and may not accurately represent the nature of their discussion.
Use neutral language to describe the analysts' discussion about LeBron James.
A headline that does not accurately reflect the content of the article.
The headline suggests a petty argument, which may not be representative of the actual discussion between Sharpe and Perkins.
Revise the headline to more accurately reflect the content and tone of the article.
- 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.