As with most things Twitter, fighting spam messages is not as easy as it is in other parts of our online lives. The traditional machine learning models and other techniques used to learn what spam looks like and classify messages don’t always work when you’re dealing with content that users expect to see in real time. However, Twitter has developed a system called BotMaker to address its unique situation — a system the company claims has results in a 40 percent reduction in spam since it was rolled out.

Engineer Raghav Jeyaraman explained BotMaker in a blog post on Thursday. Like many other systems in place among web companies, Twitter included, the trick to BotMaker is breaking it down into real-time, near-real-time and batch jobs. Essentially, a tool called Scarecrow tries to stop spam messages before they’re written to Twitter, by spotting problem account names or URLs, for example. Next, a tool called…