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

Automating dynamic consent decisions for the processing of social media data in health research