Fighting spam with BotMaker
blog.twitter.com
blog.twitter.com
It's easy to reduce spam by X% if you don't care about increasing your false-positives rate.
I'm also curious what ways users have to flag false-positives on twitter. It's easy to 'block or report', but is there a spam box to inspect and mark things that were wrongly classified? If there isn't an easy way, then it's going to be much harder to even measure false positive rates, let alone reduce it.
What I'd really like to see is the option to submit a report for the false positive. I'm sure the data mining is picking up on not only my initial submission, but also reattempts with altered content to try and bypass their greedy algos.
Needless to say it's a bit of a bummer when you curate a tweet only to be denied sending. I think they have it out for .CO url shorteners :)
If it's of any use, I use Tweetdeck.
One example: if you have a rule "sending the same URL 100 times in a row is spamming" then you'll let 99 spams through before you identify that they were spams.
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tl;dr: it's all public data, meaning spammers know everything they do about the corpus, and it can't add significant latency.
Twitter built a DSL for writing spam detection algorithms.
Only time will tell I guess. From the article it would appear that they aren't doing anything magically different with regards to classification of twitter-spam, but they have found a way to deal with the volume of classification tasks in a pertinent manner. It also gives them a way to quickly respond to new types of spam attacks.
Very interesting. They should consider opening it up as an Askimet competitor. The difference between a blog comment and a twitter post is negligible.
> publicizing how this works will come back to bite them in the butt
I didn't see any rules or heuristics published. Merely that they employ a multi-stage filter (as any engineer could imagine), and that they codify their ruleset with a human-readable DSL (which is kind of interesting, but also kind of weird). > The difference between a blog comment and a twitter post is negligible.
The difference in textual content between a tweet and blog comments falling within a similar character length are perhaps negligible, though there is often no such length constraint in place for blogs. Comment spam strategies are free to vary string length to optimize for evasion, click through, proliferation, and other criteria. I would also argue there is a different demographic distribution between Twitter and blogs with regard to readership and participation.That said, Twitter just outlined a number of reasons why their case is special. They are a high-availability, high-volume, low-latency service. Twitter's spam solution was designed to handle their very special set of constraints. I think a multi-stage filter complete with asynchronous post-processing jobs would be a bit much for your average Wordpress blog. People just starting out with (say, PHP) probably can't fathom a multiprocess deployment architecture. Not to say they couldn't, but the journey is a long one.
That's why he suggested an Akismet competitor - in case you haven't used it, Akismet is a SAAS solution that filters your comments on their servers, not on your own, so you don't have to worry about the architecture or deployment.