The rise of misinformation, biased reporting, and fake news has made it more difficult for any monitoring solution provider, from media intelligence to risk intelligence platforms, to deliver accurate insights. With 90% of the content available on the internet expected to be produced with the help of AI in 2025, sorting fact from fiction has become a critical challenge with far-reaching implications.
At Webz, we are committed to equipping our customers with reliable and transparent data that powers accurate insights. That’s why we’re introducing the Trust Category into our News API offering. The Trust Category is set to include classifications that help your solution differentiate between reliable and unreliable content and sources.
The first two trust categories we are rolling out are:
This is just the beginning. We plan to expand the trust.category field with additional tags to further support solution providers in assessing content reliability.
Our trust.category field is a new classification mechanism designed to help customers filter content based on trustworthiness. With this first set of tags, you can either include or exclude specific categories, tailoring their data streams to meet their needs.
For example:
By integrating this functionality, we provide more control and transparency over the data your solution relies on to generate accurate insights.
Our customers serve a range of industries, each with unique challenges in navigating misinformation and biased reporting. Here’s how they’ve told us they plan to use these new trust category tags:
By filtering out unreliable sources, risk intelligence solutions can detect and verify threats faster, minimizing misinformation-driven alerts, and reduce false positives, enabling their customers to focus on real risks to their business.
Separating factual reporting from misleading or fake content helps media intelligence solutions provide a comprehensive view of the discussions around brands, competitors and market trends – whether they are organic or agenda-driven.
Markets react to news in real time. Filtering out fake or satirical sources ensures that financial forecasting platforms and trading algorithms are generating insights that are based on accurate information, reducing risk and improving predictive modeling.
You can immediately start using the new Trust Tags by adding the trust.category filter to your API queries. We also invite you to discuss with our team which sources are included in these categories – and why. You can also check our documentation for implementation details.
If you’re looking for data you can trust to drive reliable insights, get in touch with us for a demo. Our dedicated data expert team is ready to help you get started and optimize our data for your specific use case.
The introduction of Trust Tags marks a significant step in our mission to help intelligence solution providers get the data they need to deliver reliable insights, amid the growing threat of misinformation, biased reporting, and fake news – at scale. Stay tuned as we expand our trust.category field with more classifications in the coming months!
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The rise of misinformation, biased reporting, and fake news has made it more difficult for any monitoring solution provider, from media intelligence to risk intelligence platforms, to deliver accurate insights. With 90% of the content available on the internet expected to be produced with the help of AI in 2025, sorting fact from fiction has […]
