We develop a methodology to automate creating imaginary people, referred
to as personas, by processing complex behavioral and demographic data
of social media audiences. From a popular social media account
containing more than 30 million interactions by viewers from 198
countries engaging with more than 4,200 online videos produced by a
global media corporation, we demonstrate that our methodology has
several novel accomplishments, including: (a) identifying distinct user
behavioral segments based on the user content consumption patterns; (b)
identifying impactful demographics groupings; and (c) creating rich
persona descriptions by automatically adding pertinent attributes, such
as names, photos, and personal characteristics. We validate our approach
by implementing the methodology into an actual working system; we then
evaluate it via quantitative methods by examining the accuracy of
predicting content preference of personas, the stability of the personas
over time, and the generalizability of the method via applying to two
other datasets. Research findings show the approach can develop rich
personas representing the behavior and demographics of real audiences
using privacy-preserving aggregated online social media data from major
online platforms. Results have implications for media companies and
other organizations distributing content via online platforms.