Machine Learning Susses Out Social-Network Fraud 42
CowboyRobot writes "Machine learning techniques can be used to detect fraud and spies on social networks based on certain features, such as the number of followers and the number of devices used to access the network. Certain characteristics of social-network accounts have a high correlation with fraud and can be used to differentiate between real and fake accounts, a researcher presenting at the SOURCE Boston Conference said this week. Using machine learning techniques, Vicente Diaz, a senior security analyst with security software firm Kaspersky Lab, found that seven characteristics of Twitter profiles could identify fraudulent accounts 91% of the time. The number of devices from which a user accesses the service, the ratio of followers to people following an account, the average number of tweets to each person, and the number of tweets to an unknown receiver are all features that correlate strongly to fraudulent accounts, he says."
Re:So I would be a fraud... (Score:4, Informative)
"So I would be a fraud if I had a facebook account."
Precisely. There are several things wrong with trying to actually use this in the real world.
(1) 91% is not nearly good enough. Period.
(2) Even if it were 99.9% accurate, it would still not be good enough. Because it runs into the base rate fallacy [wikipedia.org].
(3) Similar but not related to the base rate fallacy, is that a statistical correlation between datasets of millions says nothing about an individual account.