Casual Encounters of the Third Kind: A Bayesian Classifier for Craigslist

by Brian Detweiler

Introduction

Are you a single male?  Are you looking for no strings attached sex?  Are you looking for an easy way to pick up easier women?  Then look no further than Craigslist Casual Encounters!

It's the place to find thousands of single, horny women looking for exactly the same thing!  ...or is it?

In this article, I scientifically examine the myth of Craigslist Casual Encounters.

The focus has been placed on w4m (women4men) in the Omaha, Nebraska location.  This research could (and should) be expanded to other cities, as well as other keywords.

The Idea

I have long held the belief that sexually frustrated men everywhere are being taken advantage of in our society.

Everything from girls asking for free drinks at bars to pay websites like AdultFriendFinder.com charging money for finding women to hook up with.  Craigslist, however, is free, minimalistically designed, and used by millions of people around the globe.  It seems like the perfect way for someone to fulfill their desires and not be taken advantage of.

But where there are trusting people, there will always be enterprising no-goodnicks trying to ruin the fun for everyone.

Enter the Craigslist spammer.

How does one spam on Craigslist?  There are two ways.  The obvious, and quickly detected method of dropping website links directly in a posting, and the more underhanded, legitimate looking post that waits for users to email them so they can send them deceptive spam emails.

Make no mistake, this is spam.  But unlike traditional spam, we are essentially opting in by viewing and replying to postings.

Unfortunately, traditional spam filters work by catching incoming emails.  The popular Bayesian spam filter keeps a database of words and their "spaminess."  So, how could we apply that to Craigslist, to save us the trouble of unwittingly "opting in?"

Bayesian spam filters must be trained.

We must start off with decently sized corpuses of spam and ham text.  Then we are responsible for training the filter by telling it if a body of text is good or bad. When dealing with email, the case is as simple as collecting the email, going through it one by one, and flagging the spam.  With Craigslist though, we are dealing with a website.  We will have to go to Craigslist, rather than Craigslist coming to us.

The plan is relatively simple: scrape Craigslist at arbitrary time intervals (every three minutes seems reasonable), logging entries into a database.  When an entry becomes "flagged," that is logged too.  The theory being, if a posting is flagged, it is likely spam.  There is a small problem with this theory, and I will expand on it later, but for now, let's assume any entry that is flagged is, indeed, spam.

The Implementation

PHP works nicely for this project.

We can use Curl to scrape Craigslist and store the results in a PostgreSQL database.  We simply add it to our crontab and let it run for a few months (yes, a few months).  Then, when we have enough data (5,500 records is a good sample size, though Paul Graham suggests more like 8,000 - 4,000 spam, and 4,000 ham), we can finally write our Bayesian filter.

Here is the crontab:

0,3,6,9,12,15,18,21,24,27,30,33,36,39,42,45,48,51,54,57 * * * * php /path/to/clauto.php >/dev/null

For those unfamiliar with Bayesian classification, read Paul Graham's famous essay in which he discusses the virtues of statistical spam filtering.¹

Essentially, the way this works is by taking two corpuses of text (one that is predetermined to be spam, and one that is predetermined to be ham), we just need to store the individual tokens into a hash map and keep track of how many are spam vs. ham.

Then, using Bayes' Rule, we can calculate the probability that a posting is spam given an "interesting" word in that text.

A simple implementation can be found at: www.shiffman.net/teaching/a2z/bayesian³

I have translated it into PHP, which can be found find at: code.google.com/p/ce3k-bayesian-filter⁵

So, each time we fire it up, it pulls out all the posts in the database, stores them into a hash table as individual tokens, and then that is our lookup table.

Then, it hits Craigslist, reads through each post, and does the statistical comparison on them.  If a post is lower than 90% spam probability (we're being generous here), it gets displayed along with its probability.

Findings

The statistical filter looks to be working with great accuracy, just as Graham had mentioned it would on email spam.  But some of my findings came before I even wrote the filter, by just examining the raw data.

Currently, my database has a total of 5,545 postings, of which, 3,936 have been flagged (likely spam).

That is, almost 71% of all postings are not legit.

Furthermore, I kept track of which postings had pictures.  Given that most girls who post on Casual Encounters would die if anyone knew about it (God forbid anyone find out they like sex), I reasoned that it would be rare to see a legitimate post containing a picture.  That was also proven in the statistics.

Of the 4,565 postings with pictures, 3,468 were flagged (almost 76%).

In the current implementation, this is not taken into account, but if we could assign a weight to postings with pictures, this could add to the accuracy.

Caveats

The biggest concern I had when doing this was determining how to define spam.

The only way you could be 100% certain if a post was spam would be to reply to it and get an obvious spam email in return.  I did attempt this method in the beginning, but found it to be extremely inefficient for two reasons:

1. The mail host (Gmail in my case) puts a cap on the number of emails sent out in a given time period, so as to curb spam.  We should all be thankful for that, but the rapid fire-ness of my script was getting me rate-limited pretty quickly.
2. Craigslist also curbs spam in this way.

I should also mention the third reason; this is slightly unethical, actually making me a spammer.  So I scrapped this idea early on, and decided that anything that gets flagged would be considered spam.

Unfortunately, this is far from accurate.

Many legitimate posts will get flagged for no reason whatsoever.  Maybe the girl doesn't reply to someone so he gets mad and flags her.  Maybe someone flags the wrong post.  Maybe someone is mischievous.  Whatever the case, it's unfortunate, but it is the best method we have right now.

Fortunately, it is not often that a spam post will go unflagged, so we can be reasonably sure that our ham corpus is clean.

The only thing we need worry about are false positives, and the filter is pretty inherently forgiving, per Graham's suggestion.

Hacking the Script

This script is mostly proof-of-concept and is not really fit for mass consumption.

One idea would be to provide this as a service.  A user comes to the site, enters their city, and the current postings are displayed.  Maybe even pushed out as an RSS feed.

I don't have the cash for a decent host, and I'm really not sure this isn't violating Craigslist's TOS, but I'm guessing it probably is.

Currently, Craigslist does not have an API, so we are reduced to screen scraping, which is generally frowned upon, legal or not.

Another idea I had was to write a Greasemonkey script or Firefox add-on that would do all the filtering as you went to the site, but this could prove difficult for a couple of reasons.

The filtering relies on the subject and the body of the post.  On the main listings page, we are only given the subject, so we would have to do an Ajax call to get the body.  The other, bigger, problem is memory.  I had to increase PHP's memory space to around 100 MB to satisfy the requirements of the hash table.  Keeping such a hash table around in memory in Firefox does not sound like something anyone would want.

Going back to the issue of not being 100% sure something is spam; even though it's been flagged, I did consider using fuzzy logic to assist in assigning values to the tokens, assigning an arbitrary precision to spam vs. ham.

For instance, saying that we are only 75% sure that everything in the spam corpus is actually spam, we could scale the percentage that a word is spam.  This was only briefly considered, but I decided that I was happy with the way things were without it.

Conclusion - Not a Happy Ending

Sorry, gentlemen.

It appears that Craigslist is, in fact, not the Holy Grail.  Using Bayesian classification, however, can greatly cut down on time wasted writing to spammers.

There are legitimate people on the site.  The trouble is wading through all the illegitimate posts and finding the real ones before somebody else does.

So, if you're going to use Casual Encounters, why not increase your odds?  Just once, I'd like to hear that mathematics got someone laid.

Footnotes

1. A Plan for Spam  by Paul Graham
2. Better Bayesian Filtering  by Paul Graham
3. Bayesian Filtering
4. Bayesian Spam Filtering
5. Casual Encounters of the Third Kind  by Brian Detweiler