Media Manipulation and Bias Detection
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HonestyMeter - AI powered bias detection
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Investigation
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.
Exaggerating or sensationalizing events to attract attention.
The use of phrases like 'gruesome private bus inferno' and 'horrific accident' sensationalizes the event.
Use more neutral language to describe the accident, such as 'bus fire' or 'accident'.
Making claims without sufficient evidence.
The article claims that the consignment of phones 'probably added to the intense fierceness and speedy propagation of the blaze' without providing concrete evidence.
Provide evidence or expert opinions to support the claim about the phones contributing to the fire.
Using language that shows bias towards one side.
The article describes the driver as 'greeted by some as having supposedly saved numerous passengers' which implies doubt and bias.
Use neutral language to describe the driver's actions, such as 'some passengers credit the driver with helping them escape'.
- 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.