Media Manipulation and Bias Detection
Auto-Improving with AI and User Feedback
HonestyMeter - AI powered bias detection
CLICK ANY SECTION TO GIVE FEEDBACK, IMPROVE THE REPORT, SHAPE A FAIRER WORLD!
USDA data/official estimates
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 figurative or emotionally colored language that can dramatize otherwise neutral information.
1) "The USDA on Monday caught the corn market wrong-footed by increasing its estimate of the 2025 U.S. harvest to a new record..." 2) "For corn, this was a rip-the-Band-Aid-off event and these are probably the worst numbers we're going to see." These phrases add a slightly dramatic tone to what is essentially a technical adjustment in supply estimates and market reaction. While they are attributed to an analyst and are common in market commentary, they introduce emotional framing (surprise, pain, finality) beyond the bare facts.
Replace "caught the corn market wrong-footed" with a more neutral description such as: "The USDA on Monday surprised some traders by increasing its estimate of the 2025 U.S. harvest to a new record..." or "The USDA on Monday increased its estimate of the 2025 U.S. harvest to a new record, contrary to many analysts’ expectations."
Clarify the analyst’s metaphor in more neutral terms, for example: "For corn, this report may represent the most bearish set of numbers in the near term," said Ted Seifried, chief strategist at Zaner Group.
If keeping the metaphors, explicitly separate them from the reporter’s voice, e.g.: "Traders described the report as a 'rip-the-Band-Aid-off' event, suggesting they believe the most negative supply news may now be priced in."
Presenting a complex situation as more definitive or settled than the evidence clearly supports.
"For corn, this was a rip-the-Band-Aid-off event and these are probably the worst numbers we're going to see." This statement, while clearly attributed to an analyst, implies that the current USDA figures are likely the most negative possible, which may oversimplify future uncertainty in weather, policy, and demand. It is an opinion framed as a probabilistic outlook but could be read as more definitive than warranted.
Add explicit uncertainty markers and context, e.g.: "For corn, this was a rip-the-Band-Aid-off event and, in his view, these are probably the worst numbers we're going to see, assuming no major negative surprises in future reports," said Ted Seifried.
Clarify that this is one analyst’s perspective among many: "Some analysts, including Ted Seifried, see the report as potentially marking the peak in bearish supply news for corn, though others caution that future revisions and weather developments could alter the outlook."
Include a brief note on uncertainty: "Market outcomes remain subject to changes in weather, policy, and global demand, which could lead to further revisions in supply estimates."
- 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.