I'm not sure that quite addresses the problem here.

After all there is no clear definition of 'what dogs look like' (in the sense of a collection of logical rules), but deep learning models excel at detecting them, when provided with enough positive examples.

If it's possible for humans to agree on whether a given article is clickbait or not, we should be able to put together an adequate dataset for training a system to classify them too. From the linked article I am unable to discern how the training dataset was labelled.

In other words, the fact that 'clickbait' is a nebulous concept shouldn't preclude machine learning from being able to detect it.

The core of the definition is subtly wrapped in "main purpose" -- once the attention is attracted and the link is clicked, the clickbait's job is done. So the content of the article will be lower quality and less intellectually satisfying than non-clickbait articles.

For example, if you charted "interest on clicking this link text" vs "satisfaction with article after reading", I think clickbait would be clearly in the high interest vs low satisfaction quadrant.

In 2014, Jon Stewart offered an interesting definition of clickbait:

"I scroll around, but when I look at the internet, I feel the same as when I’m walking through Coney Island. It’s like carnival barkers, and they all sit out there and go, 'Come on in here and see a three-legged man!' So you walk in and it’s a guy with a crutch."

The thing is, he was talking about BuzzFeed when he said that, and that is not what BuzzFeed does at all. BuzzFeed's editor wrote about the distinction here, and it's the most insightful article I've read on the topic:

https://www.buzzfeed.com/bensmith/why-buzzfeed-doesnt-do-cli...

People tend to consider things like lists clickbait, even though those articles usually deliver exactly what the headline suggests. (If you click on "23 photos of kittens that are just too adorable," that is what you will get.) But because it's an article that was made specifically to get traffic, people incorrectly call it clickbait.

And it often goes even further than that. On Reddit and Hacker News, commenters constantly call articles clickbait. Sometimes it's true, and there's a sensational headline that leads to a bullshit story. But just as often, the story delivers on what the headline promises, but commenters call it clickbait because the headline is slightly hyperbolic, snappy, or just plain well-written.

I would define clickbait as articles which intentionally try to disguise what you'll get out of reading them. The information is banal, but the headline makes it out to be revolutionary or shocking.

You might disagree with the details of the formulation, but I think there's pretty broad agreement that something similar is going on with clickbait.

I guess it's an inherently fuzzy concept, so quite a good fit for machine learning.

But my definition of clickbait is any link I follow where I feel like I've been tricked into the click. The link looked interesting, but I feel regret once I see the actual content.

A definition would need a bit more fleshing out, mostly about the (lack of) actual content; a long-winded page (not just text) that eventually leads to the core, which could be summarised in one line, even the article title itself. (like 'peanut butter is made out of peanuts' instead of "you'll NEVER guess this ONE SECRET peanut butter ingredient!"

Vanilla Reddit is almost garbage because of how default subreddit posts take over your front page.

What you need to do is to unsub from all default subreddits, subscribe to niche ones you like and use Reddit Enhancement Suite (RES) [1] to contain the default subreddits to what RES calles the Dashboard (basically a page where you can add lots of subreddits as individual widgets).

[1] https://redditenhancementsuite.com/

your best bet is to filter out meme subs

Seems like everyone uses the glove embeddings for any text based DL project.

yes - i am skeptical on how generalizable the final model is - given lots of features (numerical and text) are closely linked to same domain.

That wasn't clickbait. Arguably it was worse. "You'll never guess what happens when she starts to sing!" isn't likely to contribute to increased military tensions between nuclear powers.

To put it as a triviality: just because two things are bad doesn't mean they have to be bad in the same way.

I also wouldn't classify that story as "fake news"[0]. Those were things like "Revealed: Obama says Clinton would be terrible president", or "Revealed: Trump under investigation by European Court for Human Rights". Those were straightforward false claims, with zero actual sourcing, by people who knew they were lying. This Washington Post article was shitty reporting, using thin sources, that fit a currently popular hysteria. And it was completely inaccurate. But the authors didn't sit down and say "what can we make up." They got some sources and didn't do any due diligence, because it was too hard to pass up on such a juicy story.

I'm not wedded to the idea that these articles aren't fake news, but I'm confident it doesn't make sense to call them clickbait.

[0] Of course, this relies on the idea that fake news doesn't just mean "news that is wrong", which has been with us forever, but more about a social media driven trend within the past year or two.