  In our generation, there is an undoubted rise in the use of social media and
specifically photo and video sharing platforms. These sites have proved their
ability to yield rich data sets through the users' interaction which can be
used to perform a data-driven evaluation of capabilities. Nevertheless, this
study reveals the lack of suitable data sets in photo and video sharing
platforms and evaluation processes across them. In this way, our first
contribution is the creation of one of the largest labelled data sets in Flickr
with the multimodal data which has been open sourced as part of this
contribution. Predicated on these data, we explored machine learning models and
concluded that it is feasible to properly predict whether a user is a
professional photographer or not based on self-reported occupation labels and
several feature representations out of the user, photo and crowdsourced sets.
We also examined the relationship between the aesthetics and technical quality
of a picture and the social activity of that picture. Finally, we depicted
which characteristics differentiate professional photographers from
non-professionals. As far as we know, the results presented in this work
represent an important novelty for the users' expertise identification which
researchers from various domains can use for different applications.
