Media Manipulation and Bias Detection
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HonestyMeter - AI powered bias detection
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Sydney Swans
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 language that implies a judgment or position rather than presenting just the facts.
Tom Papley's comments about Essendon's physicality being a 'facade' and the description of Peter Wright's bump as 'clumsy' and 'head-high' imply a negative judgment of Essendon's actions.
Avoid using loaded terms such as 'facade' without attribution.
Describe the incident involving Peter Wright more neutrally, for example, 'Peter Wright is under review for a bump on Harry Cunningham'.
The use of exciting or shocking stories or language at the expense of accuracy, in order to provoke public interest or excitement.
The phrase 'spicy TV interview' and the focus on the physical confrontations and 'spot fires' after the game may exaggerate the contentiousness of the events for dramatic effect.
Use more neutral language to describe the interview, such as 'candid' instead of 'spicy'.
Present the physical confrontations as part of the game's events without implying excessive drama.
- 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.