Solve Fake News with Advertising

Here is my Salk-like, free solution for fake news.

A market-based pricing model, of course, driven by artificial intelligence.

Using Sentiment Analysis...

Natural language processing (NLP) can rank sentiment, identifying bias and degrees of polarizing vocabulary and the like.

A basic task in sentiment analysis is classifying the polarity of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. - Wikipedia - Sentiment Analysis

...Score Integrity of the Article...

Articles get read by an Integrity Reader, the AI component, and assigned a score.

...Less Reward for More Polarizing Spaces...

Advertisers pay more in spaces where language used has less bias and polarization, value, etc.

...Algo Up....

Positive multipliers for novelty, breaking a story, reading level, author Integrity score, dept IS, org score, novel supporting sources, etc.

...Algo Down....

Negative multipliers for retractions, accuracy, reading level, author Integrity score, dept IS, org score, etc.

...Add Transparency.

Article scores are public.


Over time news gets expanded while injecting bias. (We're humans. It's gonna happen.)

A quick sketch.

The most objectively informative articles are rewarded at 100X the least valuable are.


If that's all solved then, what else do you want to fix?