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.
Using emotionally charged words to influence the reader's perception.
"Absolutely disgusted" and "I just think it’s ridiculous that Coles would rather lose out on $450 [$297.54 USD] in shopping rather than own up to a mistake."
Replace 'absolutely disgusted' with 'very upset' or 'displeased'.
Replace 'ridiculous' with 'unfortunate' or 'disappointing'.
Making claims without providing evidence.
"I hate to think how many other people have been caught out because of it."
Provide data or evidence to support the claim, or rephrase to indicate it is a personal opinion, e.g., 'I wonder if other people have experienced this issue.'
Using emotional appeals to persuade the reader.
"I hate to think how many other people have been caught out" and "That’s ridiculous"
Focus on factual information and avoid emotional appeals, e.g., 'This issue may have affected other customers as well.'
- 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.