  Several projects have shown the feasibility to use textual social media data
to track public health concerns, such as temporal influenza patterns or
geographical obesity patterns. In this paper, we look at whether geo-tagged
images from Instagram also provide a viable data source. Especially for
"lifestyle" diseases, such as obesity, drinking or smoking, images of social
gatherings could provide information that is not necessarily shared in, say,
tweets. In this study, we explore whether (i) tags provided by the users and
(ii) annotations obtained via automatic image tagging are indeed valuable for
studying public health. We find that both user-provided and machine-generated
tags provide information that can be used to infer a county's health
statistics. Whereas for most statistics user-provided tags are better features,
for predicting excessive drinking machine-generated tags such as "liquid" and
"glass" yield better models. This hints at the potential of using
machine-generated tags to study substance abuse.
