[unmdplyr]

I think it's possible. I know for certain there are techniques to identify "click-baity" text snippets. My partner is currently working on a data model now and she has already had some reasonable success. Imagine what a company that hires several 10s or even 100s of data scientists can achieve?

From there on, it's just a matter of highlighting these successful matches constantly in your feeds.

I wouldn't put it past that.

[rendall]

Interesting idea.

I see 2 potential problems.

If the approach would be content-neutral - i.e. simply relying on the form of the information (eg misspellings in the title, many exclamation points, and such) to distinguish that which is more likely to be fake - then there could be a race condition where the misinformation purveyors learn and subvert the algorithm followed by the misinformation identifier incorporating the new forms of misinformation, and so on. In the meantime, true information purveyors would also need to be aware of this algorithm so as not to be falsely labeled. Think of the race in SEO for an example

If the misinformation identifier uses the content of the information to label misinformation, then the identifier itself is as open to bias and opinion as anyone else

[daniellarusso]

You compared to SEO, but I was thinking about email spam filtering.

Some of the best spammers have better practices than the legitimate companies sending email.