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!
Residents and taxi operators (protesters)
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 language or examples to elicit sympathy or anger rather than focusing purely on neutral, verifiable facts.
Examples appear in quoted speech from residents and taxi operators: 1) "The road is in a deplorable condition and the people of Brokenhurst have been suffering for the past four years. We have endured until we can’t anymore." 2) "We are having a peaceful protest so we can get to those in authority about the condition of the road. We have a situation where even those who are working minimum wage are affected as they are unable to get transportation to go home in the evenings..." 3) "We are having a bad task with this road as taxi operators. We vent our frustration to the Member of Parliament and the councillor, but it is just a run-around, so we had to resort to the last option." These statements emphasize suffering, frustration, and hardship in emotive terms. While this is natural in interviews and is clearly attributed to speakers, it still functions as an appeal to emotion for readers.
In the reporter’s narrative, balance emotional quotes with neutral, concrete details: e.g., add specific measurements (length of bad road, number of potholes, accident statistics, repair history) to ground the emotional claims in data.
When quoting emotional language, consider adding brief clarifying context: e.g., "Residents, who say they have faced frequent vehicle damage and longer commute times, described the situation as 'deplorable' and said they have 'endured until [they] can’t anymore.'"
Include any available official data or prior statements from authorities about the road condition and repair plans to ensure that emotional accounts are complemented by factual background.
Drawing or presenting broad conclusions based on limited or anecdotal evidence.
Some quoted phrases generalize the experience of the entire community without clear evidence: 1) "The people of Brokenhurst have been suffering for the past four years." 2) "We have endured until we can’t anymore." 3) "This is the only way to let our voices be heard and for the necessary authorities to take action…" 4) "We vent our frustration to the Member of Parliament and the councillor, but it is just a run-around, so we had to resort to the last option." These statements imply that all residents share the same level of suffering, that no other avenues are effective, and that authorities uniformly give a "run-around". The article does not provide broader survey data or multiple independent sources to substantiate these sweeping claims, though they are clearly presented as individual opinions.
Attribute generalizations explicitly as personal or group perceptions: e.g., change to "Many residents say they have been suffering for the past four years" or "Residents who joined the protest say this is the only way to let their voices be heard."
Add a brief note that the claims reflect the views of those interviewed: e.g., "Protesters allege that previous complaints to the MP and councillor have not led to improvements."
If available, include additional data or perspectives (e.g., comments from other residents, records of previous repair works, or official complaint logs) to show whether these generalizations are widely supported or contested.
Giving more space, detail, or sympathetic framing to one side of a dispute than to others, which can subtly favor that side.
The article provides multiple detailed quotes from residents and taxi operators describing hardship and frustration, while the political representatives’ side is represented only by Councillor Karl Smith. The Member of Parliament (Rhoda Crawford) and the Ministry of Local Government/Minister Desmond McKenzie are mentioned but not quoted or paraphrased with any direct response. The structure therefore gives: - Protesters: several quotes, specific grievances, and emotional framing. - Authorities: one councillor’s response, focused on process (estimates, writing to ministry) and acknowledging the problem. This is not overtly biased, but the asymmetry in voices and detail can subtly favor the protesters’ narrative and leave the impression that higher-level authorities are unresponsive without giving them a chance to respond.
Seek and include a direct response or prior statement from the Member of Parliament and/or the Ministry of Local Government about the road conditions and repair plans. If unavailable by deadline, explicitly state that attempts were made: e.g., "Attempts to reach MP Rhoda Crawford and the Ministry of Local Government for comment were unsuccessful up to press time."
Provide any available background on funding constraints, repair schedules, or previous works in the area to contextualize the councillor’s claim that he is unlikely to be able to fix the road with current parish council allocations.
Clarify the scope of the councillor’s responsibility versus that of central government, so readers understand institutional limits rather than inferring simple neglect.
- 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.