  Twitter provides an open and rich source of data for studying human behaviour
at scale and is widely used in social and network sciences. However, a major
criticism of Twitter data is that demographic information is largely absent.
Enhancing Twitter data with user ages would advance our ability to study social
network structures, information flows and the spread of contagions. Approaches
toward age detection of Twitter users typically focus on specific properties of
tweets, e.g., linguistic features, which are language dependent. In this paper,
we devise a language-independent methodology for determining the age of Twitter
users from data that is native to the Twitter ecosystem. The key idea is to use
a Bayesian framework to generalise ground-truth age information from a few
Twitter users to the entire network based on what/whom they follow. Our
approach scales to inferring the age of 700 million Twitter accounts with high
accuracy.
