Here is my Salk-like, free solution for fake news.
A market-based pricing model, of course, driven by artificial intelligence.
Using Sentiment Analysis...
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.
Positive multipliers for novelty, breaking a story, reading level, author Integrity score, dept IS, org score, novel supporting sources, etc.
Negative multipliers for retractions, accuracy, reading level, author Integrity score, dept IS, org score, etc.
Article scores are public.
Over time news gets expanded while injecting bias. (We're humans. It's gonna happen.)
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?