
Fakernews: Builds a Markov chain using HN post titles and generates fake posts - pjf
https://github.com/mb-14/gomarkov/tree/master/examples/fakernews
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akhilrex
This has existed for quite some time.

[https://twitter.com/HNTitles](https://twitter.com/HNTitles)

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ainar-g
A lot of these are so accurate it's uncanny.

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heinrichf
The first example just spat out
[https://news.ycombinator.com/item?id=17739320](https://news.ycombinator.com/item?id=17739320)
with "Show HN" in front of it.

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Sohcahtoa82
Similar to this on reddit is the Subreddit Simulator[0].

The subreddit is populated entirely by bots that make posts based on Markov
chains, each one trained from the posts on a specific other subreddit. The
bots even post comments based on the comments from their assigned subreddit.
Some of the results are hilarious.

[0]
[https://www.reddit.com/r/SubredditSimulator](https://www.reddit.com/r/SubredditSimulator)

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amelius
I'd like to see this applied to patents.

And then test how many of them survive the scrutiny of USPTO officers.

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aldoushuxley001
> Feds Threatened to Fine Yahoo $250K Daily for Not Being Able to Print
> Automobile-Sized Metal Objects

haha, Love it!

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torstenvl
What happens when you pit Markov chain fake news against Markov chain Donald
Trump?

[https://filiph.github.io/markov/](https://filiph.github.io/markov/)

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presscast
This seems to perform better than the OP's link. Does anybody know why that
is? Are there certain forms of data for which Markov chains perform better?

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torstenvl
As the sibling comment talks about, I think DJT's tweeting pattern helps. He
often uses short sentences, so with Markov order 2 (every three words
generated is a quote, more or less), there number of seams per sentence is
lower. (By seams, I mean places where the generated text seems disjointed
because the Markov chain consists of two non-semantic or semantically
ambiguous tokens.)

