[olympusmons]

Lets brainstorm.

Perhaps one mathematical solution to the problem is to score pages based on the average time spent for each article. My hypothesis is that clickbait articles will score very low because the article will attract a lot of clicks from people who are not interested in the actual content but only the headline.

In theory, you could do this by deploying a tracker across the web similar to a Google analytics that performs the measurement. But in practice, of course, this isn't possible.

You might be able to approximate the score by building a browser extension that makes the measurement. A browser extension might also a good way to output results. It could, for instance, colour-code links (on mouseover) to help you spot click-bait before you click it!

[brownbat]

There's a plugin for Wikipedia where if you hover over a linked term, it will show the first paragraph from that article, usually all you need.

We just need something like that for the web.

[olympusmons]

I like your solution because it doesn't (necessarily) require a backend and would therefore be much simpler to implement.

There are several libraries that extract article text reasonably well. I believe browser extensions are written mainly in HTML/JS/CSS. Would the following node library suffice? https://github.com/ageitgey/node-unfluff

There's just the question of retrieval speed. I suspect simple HTML retrieval is an order of magnitude faster than normal page rendering due to ads, javascript, etc. So the client-only solution you've proposed might indeed be sufficient!

[kalleboo]

I use QuickLook in Safari on OS X and iOS like that (OS X: triple-finger-tap to get a link preview, iOS: Force touch). It only gets ruined by those "subscribe!" full screen popups.