  Social networks give free access to their services in exchange for the right
to exploit their users' data. Data sharing is done in an initial context which
is chosen by the users. However, data are used by social networks and third
parties in different contexts which are often not transparent. We propose a new
approach which unveils potential effects of data sharing in impactful real-life
situations. Focus is put on visual content because of its strong influence in
shaping online user profiles. The approach relies on three components: (1) a
set of concepts with associated situation impact ratings obtained by
crowdsourcing, (2) a corresponding set of object detectors used to analyze
users' photos and (3) a ground truth dataset made of 500 visual user profiles
which are manually rated for each situation. These components are combined in
LERVUP, a method which learns to rate visual user profiles in each situation.
LERVUP exploits a new image descriptor which aggregates concept ratings and
object detections at user level. It also uses an attention mechanism to boost
the detections of highly-rated concepts to prevent them from being overwhelmed
by low-rated ones. Performance is evaluated per situation by measuring the
correlation between the automatic ranking of profile ratings and a manual
ground truth. Results indicate that LERVUP is effective since a strong
correlation of the two rankings is obtained. This finding indicates that
providing meaningful automatic situation-related feedback about the effects of
data sharing is feasible.
