  Social media have become a rich source of data, particularly in health
research. Yet, the use of such data raises significant ethical questions about
the need for the informed consent of those being studied. Consent mechanisms,
if even obtained, are typically broad and inflexible, or place a significant
burden on the participant. Machine learning algorithms show much promise for
facilitating a 'middle ground' approach: using trained models to predict and
automate granular consent decisions. Such techniques, however, raise a myriad
of follow-on ethical and technical considerations. In this paper, we present an
exploratory user study (n = 67) in which we find that we can predict the
appropriate flow of health-related social media data with reasonable accuracy,
while minimising undesired data leaks. We then attempt to deconstruct the
findings of this study, identifying and discussing a number of real-world
implications if such a technique were put into practice.
