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Deep Learning on Title and Content Features to Tackle Clickbait

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Re: Deep Learning on Title and Content Features to Tackle Clickbait

#21

Post by narrator »

trevyn wrote: post #15
grabcocque wrote: post #8

The problem is, we don't have a clear definition of what clickbait is. nouninformal (on the Internet) content whose main purpose is to attract attention and encourage visitors to click on a link to a particular web page. But that's basically everything on the web.

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 th…

One problem with that definition is that intellectual satisfaction is disproportionately affected by confirmation bias. For instance, an article entitled "10 reasons why getting elected means the end of America" might be clickbait to some, but not others depending on what contains.

Re: Deep Learning on Title and Content Features to Tackle Clickbait

#22

Post by astrodust »

BooglyWoo wrote: post #12
grabcocque wrote: post #8

The problem is, we don't have a clear definition of what clickbait is. nouninformal (on the Internet) content whose main purpose is to attract attention and encourage visitors to click on a link to a particular web page. But that's basically everything on the web.

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…

Just as "dogness" is a factor, so is "clickbaityness". You're right, this is all about thresholds.

Re: Deep Learning on Title and Content Features to Tackle Clickbait

#23

Post by astrodust »

Cthulhu_ wrote: post #11
grabcocque wrote: post #8

The problem is, we don't have a clear definition of what clickbait is. nouninformal (on the Internet) content whose main purpose is to attract attention and encourage visitors to click on a link to a particular web page. But that's basically everything on the web.

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!"

Is that a deep buried lede with a teaser headline?

The thing that irritates me the most is these absurd ads that make bold claims and never deliver on what they're advertising. Even if you're interested in what they're offering, you're willing to take the bait, it's a lost cause, they never fulfill their promise. It's just carousels of bullshit jammed full of more ads.

Re: Deep Learning on Title and Content Features to Tackle Clickbait

#24

Post by visarga »

I'd like a system to filter out fluff threads on reddit. It would reject easy-consumption content such as images, gifs and short vids, or anything shorter than 60 seconds; also, low quality comments (short, aggressive, memes, etc).

Reddit is a gold-mine of interesting content, but it is flooded with fluff and garbage to the point where it becomes a problem to find the good parts.

I'm wondering why they don't use more machine learning magic on the site. There are multiple machine learning papers based off the reddit comment corpus.

Re: Deep Learning on Title and Content Features to Tackle Clickbait

#26

Post by make3 »

visarga wrote: post #24

I'd like a system to filter out fluff threads on reddit. It would reject easy-consumption content such as images, gifs and short vids, or anything shorter than 60 seconds; also, low quality comments (short, aggressive, memes, etc). Reddit is a gold-mine of interesting content, but it is flooded with fluff and garbage to the point where it becomes a problem to find the good parts. I'm wondering why they don't use more…

your best bet is to filter out meme subs

Re: Deep Learning on Title and Content Features to Tackle Clickbait

#27

Post by visarga »

make3 wrote: post #26
visarga wrote: post #24

I'd like a system to filter out fluff threads on reddit. It would reject easy-consumption content such as images, gifs and short vids, or anything shorter than 60 seconds; also, low quality comments (short, aggressive, memes, etc). Reddit is a gold-mine of interesting content, but it is flooded with fluff and garbage to the point where it becomes a problem to find the good parts. I'm wondering why they don't use more…

your best bet is to filter out meme subs

There is also a need to find interesting threads outside a known list of subs, or to filter out some parts of otherwise good subs.

Re: Deep Learning on Title and Content Features to Tackle Clickbait

#28

Post by imanewsman »

grabcocque wrote: post #8

The problem is, we don't have a clear definition of what clickbait is. nouninformal (on the Internet) content whose main purpose is to attract attention and encourage visitors to click on a link to a particular web page. But that's basically everything on the web.

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.

Re: Deep Learning on Title and Content Features to Tackle Clickbait

#29

Post by arkitaip »

visarga wrote: post #24

I'd like a system to filter out fluff threads on reddit. It would reject easy-consumption content such as images, gifs and short vids, or anything shorter than 60 seconds; also, low quality comments (short, aggressive, memes, etc). Reddit is a gold-mine of interesting content, but it is flooded with fluff and garbage to the point where it becomes a problem to find the good parts. I'm wondering why they don't use more…

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/

Re: Deep Learning on Title and Content Features to Tackle Clickbait

#30

Post by kristianc »

astrodust wrote: post #22
BooglyWoo wrote: post #12

Earlier quoted context omitted.

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…

Just as "dogness" is a factor, so is "clickbaityness". You're right, this is all about thresholds.

I often wonder what Wittgenstein would have made of today's models of machine learning / deep learning https://en.m.wikipedia.org/wiki/Family_resemblance
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