  Social media provide a wealth of information for research into public health
by providing a rich mix of personal data, location, hashtags, and social
network information. Among these, Instagram has been recently the subject of
many computational social science studies. However despite Instagram's focus on
image sharing, most studies have exclusively focused on the hashtag and social
network structure. In this paper we perform the first large scale content
analysis of Instagram posts, addressing both the image and the associated
hashtags, aiming to understand the content of partially-labelled images taken
in-the-wild and the relationship with hashtags that individuals use as noisy
labels. In particular, we explore the possibility of learning to recognise food
image content in a data driven way, discovering both the categories of food,
and how to recognise them, purely from social network data. Notably, we
demonstrate that our approach to food recognition can often achieve accuracies
greater than 70% in recognising popular food-related image categories, despite
using no manual annotation. We highlight the current capabilities and future
challenges and opportunities for such data-driven analysis of image content and
the relation to hashtags.
