Seekr evaluation algorithms offer a consistent and programmatic assessment of results in just seconds. For each result, Seekr AI analyzes the quality of information, adherence to journalistic principles, and general reliability of the author and domain source.
Each article is analyzed using several machine-learning algorithms that detect the presence of specific elements that can indicate techniques used to mislead the reader (i.e., subjectivity, absence of byline, exaggeration, clickbait, etc.) Once analyzed, the result is scored using an aggregate numerical rating based on several contributing factors. A higher presence of a particular factor may negatively impact the overall Seekr Score.
When clicking on a Seekr Score, the Seekr Score Factors appear. These factors are defined in the following table:
Factor | Description |
---|
Byline | A byline names the author(s). |
---|
Title Exaggeration | The title overstates aspects of the story. |
---|
Subjectivity | The article expresses points of view that go beyond reporting facts. |
---|
Clickbait | The title appeals to curiosity or emotion instead of describing the story. |
---|
Personal Attack | The person is attacked rather than the argument. |
---|
Lack of Site Disclosure | Websites that do not share their mission, ownership, and policies may be less reliable. |
---|