Abstract
In this chapterwe link two recent phenomena. First, innovations in technology have lowered the cost of data storage and enabled scalable parallel computing. Connected with social media, the Internet of Things applications and other sources, large data sets can easily be collected. These data sets are the basis for greatly improving our understanding of individuals and group dynamics. Second, events such as the election of Donald J. Trumpas President of the United States of America and the exit of Great Britain from the European Union have shaped public debates on the influence of psychometric micro-targeting of voters. Generally, public authorities, but also other organizations, have a very high demand for information about individuals. We combine these two streams,meaning the enormous amounts of data available and the demand for micro-targeting, aiming at answering the following question: How can big data analytics be used for psychometric profiling? We develop a conceptual framework of how Facebook data might be used to derive the psychometric traits of an individual user. Our conceptual framework includes the Facebook Graph API, a nonSQL Mongo Data Base for information storage and R scripts to reduce the dimensionality of large data sets by applying the latent Dirichlet allocation to determine correlations between reduced information with psychologically relevant words. In this chapter we provide a hands-on introduction to psychometric trait analysis and present a scalable infrastructure solution as a proof of concept for the concepts presented here. We discuss two use cases and show how psychometric information, which could, for example, be used for targeted political messages, can be derived from Facebook data. Finally, potential further developments are outlined that could serve as starting points for future research.