New social media has led to an explosion in personal digital data that encompasses both those expressions of self chosen by the individual as well as reflections of self provided by other, third parties. The resulting Digital Personhood (DP) data is complex and for many users it is too easy to become lost in the mire of digital data. This paper studies the automatic detection of personal life events in Twitter. Six relevant life events are considered from psychological research including: beginning school; first full time job; falling in love; marriage; having children and parent's death. We define a variety of features (user, content, semantic and interaction) to capture the characteristics of those life events and present the results of several classification methods to automatically identify these events in Twitter.