The researchers also introduced the new term ‘socware’, to describe a combination of "social malware," encompassing all criminal and parasitic behavior on online social networks.
The researchers found that their application took an average of .0046 seconds to classify a post, which is far quicker than the 1.9 seconds it takes using the traditional approach of web site crawling.
MyPageKeeper's more efficient classification also translates to lower costs, cutting expenses by up to 40 times.
“This is really the perfect recipe for socware detection to be viable at scale: high accuracy, fast, and cheap,” said Harsha V. Madhyastha, an assistant professor of computer science and engineering at UC Riverside''s Bourns College of Engineering.
The application, which is already attracting commercial interest, works by continuously scanning the walls and news feeds of subscribed users, identifying socware posts and alerting the users.
In the future, the researchers are considering allowing MyPageKeeper to remove malicious posts automatically.