bayes_motel – Bayesian classification for Ruby

Bayesian classification is an algorithm which allows us to categorize documents probabilistically. I recently started playing with Twitter data and realized there was no Ruby gem which would allow me to build a spam detector for tweets. The classifier gem just works on a set of text by figuring out which words appear in a category but a tweet is much more complicated than that. A tweet looks like this:

{:text=>"Firesale prices, too! RT @nirajc: Time to change your Facebook password. Hacker selling 1.5m accounts.",
:truncated=>false, :created_at=>"Fri Apr 23 18:26:51 +0000 2010", :coordinates=>nil, :geo=>nil, :favorited=>false,
:source=>"TweetDeck",  :place=>nil, :contributors=>nil,
:user=>{:verified=>false, :profile_text_color=>"666666", :friends_count=>226, :created_at=>"Wed Oct 08 07:15:23 +0000 2008",
:profile_link_color=>"2FC2EF", :favourites_count=>12, :description=>"All the news that's fit to tweet (and most that isn't)",
:lang=>"en", :profile_sidebar_fill_color=>"252429", :location=>"Brooklyn, NY", :following=>nil, :notifications=>nil,
:time_zone=>"Eastern Time (US & Canada)", :statuses_count=>981, :profile_sidebar_border_color=>"181A1E",
:profile_background_image_url=>"", :protected=>false,
:contributors_enabled=>false, :url=>"", :screen_name=>"carlfranzen", :name=>"Carl Franzen",
:profile_background_tile=>false, :profile_background_color=>"1A1B1F", :id=>16645918, :geo_enabled=>false,
:utc_offset=>-18000, :followers_count=>174}, :id=>12717456105}

As you can see, a tweet is just a hash of variables. So which variables are a better indicator of spam? I don’t know and chances are you don’t either. But if we create a corpus of ham tweets and a corpus of spam tweets, we can train a Bayesian classifier with the two datasets and it will figure out which variable values are seen often in spam and which in ham.

Some variables don’t work, statistically speaking:

  • :id, :created_at – these variables are unique for each tweet which means they are useless for classification. BayesMotel will trim any variable values that don’t appear in more than 3% of the corpus.
  • :followers_count – this is probably a pretty good spam/ham indicator in general, but not as a simple number. There are millions of possible values (@aplusk has 4.5 million followers) but we are only training on hundreds or thousands of tweets. What would be better is the binary logarithm of the followers_count to create discrete buckets: 32-64 followers = 5, 1024-2048 = 10 and so on. I’d bet any tweet with a value greater than 12 or so (i.e. 4096+ followers) is very likely to be ham.

There are additional things we could do to improve our spam detector:

  • We aren’t deep inspecting the value of the tweet text. It might be useful to have variables like “text_link_count” or “text_hashtag_count” to provide basic metrics for the tweet text content.
  • We aren’t performing any timeline checks or storing previous tweet state – spammers tend to tweet the same text over and over and their tweets all contain links. This is beyond the scope of a generic Bayesian system.

I wrote bayes_motel based on my research this last weekend. Give it a try and send a pull request if you make changes you’d like to see. The test suite gives more detail about the API and has a few thousand tweets to use as sample data. Happy coding!

7 thoughts on “bayes_motel – Bayesian classification for Ruby”

  1. Very cool (a little late to the post)… Two questions (not having looked at the code yet):

    1. Will this work on non-tweet corpii? I’m guessing it would be pretty easy to abstract it, but your examples here and on github both use tweets for them.

    2. Best guess, how much effort do you think it would take to incorporate a tournement-style classifier (a-la the fog creek blog post from a couple years back)? I’ve been contemplating something similar and this might be an excellent starting point.

    1. it works with any hash of data, where each key is considered a different variable to track, thus “multi-variate”.

      I’m not familiar with your second question so I can’t speculate.

  2. Hello. How does this compare with the “classifier” gem?
    Which one should I choose, any suggestions?

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