Media Manipulation and Bias Detection
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HonestyMeter - AI powered bias detection
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Chris Long
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.
Using shocking language or exaggeration to provoke public interest.
The title 'Your team is going to f**k you' and repeated use of strong language like 'f**k' throughout the article.
Use more neutral language in the title and throughout the article.
Avoid using profanity to maintain a professional tone.
Using language that unfairly favors one side over another.
Phrases like 'weak union' and 'the NFL can f**k you' suggest a negative bias against the NFL and NFLPA.
Provide balanced viewpoints by including statements or responses from the NFL and NFLPA.
Use neutral language to describe the actions and roles of the NFL and NFLPA.
Making claims without providing evidence or sources.
The article claims that the NFLPA kept players in the dark without providing evidence or quotes from affected players.
Include quotes or evidence from players or officials to support claims.
Provide sources or references for the claims made about the NFLPA's actions.
- 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.