In the digital age where Human Computer Interaction is creating large and entirely unique digital footprints, online accounts and activities can prove to be a valuable source of information that may contribute to verification that an asserted identity is genuine. Online social contextual data – or ‘Digital identities’ -- pertaining to real people are built over time and bolstered by associated accounts, relationships and attributes. This data is difficult to fake and therefore may have the capacity to provide proof of a ‘real’ identity. This paper outlines the design and initial development of a solution that utilizes data sourced from an individual’s digital footprint to assess the likelihood that it pertains to a ‘real’ identity. This is achieved through application of machine learning and Bayesian probabilistic modelling techniques.