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I'm a bit surprised that there are anagrams to be found. It's easy to find them if they exist, but there's no guarantee at all that there actually should be collisions.


Fermi estimate time!

Anagrams are just sentences with the same letter counts. The anagrams they're posting have 25ish letters... how many ways are there to distribute 25 balls into 26 bins? (25+26)!/25!/26! is ~250 trillion. The birthday paradox square roots that down to ~10 million, and the fact that we prefer some bins (fewer Zs, more Es) probably cuts it down even further to ~1 million.

So one anagram per million short tweets; hundreds per day. Doesn't seem too unreasonable.


Quick sanity check: most of the anagrams there are from short tweets, as you'd predict.


I'm not a statistician.

Is it really that surprising? English has plenty of redundancy; Twitter statuses have limited length.

What's surprising to me is the niceness of the found anagrams. "another math genius" / "he ain't smart enough".


> What's surprising to me is the niceness of the found anagrams.

That's because they are manually curated [0]

   Q: Is this manually curated?

    A: Mostly for issues of volume ( there are a lot of variations 
    of 'goooood mooornnniinng!', there are a lot of spam bots 
    posting subtely different versions of the same message, etc) 
    the bot doesn't automatically post every anagram it finds. 
    Essentially there's an iphone client that reviews matches, 
    which are manually approved or rejected.
[0] https://github.com/cmyr/anagramatron


I am a statistician. Maybe I should sit down and actually do some calculations.


Please do. I'd be interested.


It's actually extremely likely. The chance that any two statuses are anagrams is miniscule, and even the chance that a particular status has an anagram among all other statuses is probably small, but the chances that there are no collisions at all is tiny.

See a description of the Birthday Paradox[1] for the mathematics behind this. For example, if you put 70 people in a room, there is a 99.9% chance that two people share a Birthday.

[1]: http://en.wikipedia.org/wiki/Birthday_problem




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