The widespread adoption of networked information and communications technologies (i.e. ICTs) blurs traditional boundaries between journalist and citizen. The role of the journalist is adapting to structural changes in the news industry and dynamic audience expectations. For researchers who seek to understand what, if any, distinct role journalists play in the production and propagation of breaking news, it is vital to be able to identify journalists in social media spaces. In many cases, this can be challenging due to the limited information and metadata about social media users. In this work, we use a supervised machine learning model to automatically distinguish journalists from non-journalists in social media spaces. Leveraging Twitter data collected from three crisis events of different types, we examine how profile information, social network structure, posting behavior and language distinguish journalists from others. Additionally, we evaluate how the performance of the journalist classification model varies by context (i.e. types of crisis events) and by journalism outlets (i.e. print versus broadcast journalism), and discuss challenges in automatic journalist detection. Implications of this work are discussed; in particular we argue for the value of such methods for scaling analysis in journalism studies beyond the capacity of human coders. Employing classification methods in this context allows for systematic, large-scale studies of the role of journalists online.