How It Works

The Seekr Score

The Seekr Score measures content for reliability with speed, scale, and depth using patented machine-learning technology. While occasional inaccuracies may occur, they become increasingly rare as the technology's performance improvements accelerate over time. Seekr's algorithms are refined frequently based on regular feedback from an independent Journalist Advisory Board comprising decorated reporters and seasoned editors with over 120 years of combined experience.

Seekr holds seven patents in natural language processing (#humblebrag), meaning not only that our search technology is impressive, but also that our information evaluation capabilities are a revolutionary step for online content as well. Seekr uses core semantic analysis and natural language processing technology to find and score content. Say what?
Once generated, The Seekr Score is sorted into five levels of reliability, from very low to very high. For transparency, an explanation for each level is provided below. The Seekr Score is a number between 1 and 100 to showcase the granularity of the evaluation.
Reliable information means that facts are presented (The who, what, when) and if analysis is offered (The why), it does not stray from the facts. Reliable analysis can be presented as opinions and predictions reasonably supported by facts; that’s fair. But when the analysis strays from fact statements (Subjectivity), or uses exaggerated, sensational, or insulting language (Title Exaggeration, Clickbait, Personal Attack), the analysis becomes less reliable.
The higher presence of a Score Factor that the AI detects in an article, the lower the Seekr Score will be. Byline is an exception; if there is no Byline (named author), the overall score will be lower.
The Seekr Score does not represent likes and popularity. For example, a high score does not mean it is news everyone is reading