Media Manipulation and Bias Detection
Auto-Improving with AI and User Feedback
- Objectivity Score
- Objectivity Score
- Objectivity Score
- Objectivity Score
- Objectivity Score
- Objectivity Score
Media Manipulation and Bias Detection
Auto-Improving with AI and User Feedback
HonestyMeter - AI powered bias detection
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Competing Networks
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 exciting or shocking stories at the expense of accuracy, to provoke public interest or excitement.
The title 'CNN's Big Bet on Charles Barkley and Gayle King Has Been a Flop, and It's Only Getting Worse' is sensational, implying a dramatic failure without providing detailed context or data.
Use a more neutral title that reflects the content of the article without implying a dramatic outcome.
Use of language that is not neutral and shows author's preferences.
Phrases like 'CNN’s stock with viewers has apparently tanked' and 'People have simply had enough of CNN and its mission to spread lies, twist truths and demonize millions of conservative Americans' show a clear bias against CNN.
Rephrase to maintain a neutral tone, such as 'CNN's viewership has seen a decline' and 'Critics of CNN accuse the network of biased reporting.'
Leaving out important details that could change the perception of the story.
The article does not provide information on how the show's ratings compare to other new shows or the typical performance of shows in similar time slots.
Include comparative data to provide context for the show's ratings.
Presenting one viewpoint without the counterbalancing views.
The article focuses heavily on the negative aspects of CNN's show without mentioning any positive aspects or defenses from CNN.
Include CNN's perspective or any positive reviews of the show to provide a balanced view.
Claims that are not supported by evidence.
The claim that 'People have simply had enough of CNN and its mission to spread lies, twist truths and demonize millions of conservative Americans' is a strong accusation without evidence provided in the article.
Provide evidence for such claims or present them as opinions rather than facts.
Attempting to manipulate an emotional response in place of a valid or compelling argument.
The article uses emotionally charged language such as 'CNN’s exercise in gaiety' and 'demonize millions of conservative Americans' to provoke a reaction from the reader.
Use factual statements instead of language designed to elicit an emotional response.
- 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.