  Massive tourism is becoming a big problem for some cities, such as Barcelona,
due to its concentration in some neighborhoods. In this work we gather
Instagram data related to Barcelona consisting on images-captions pairs and,
using the text as a supervisory signal, we learn relations between images,
words and neighborhoods. Our goal is to learn which visual elements appear in
photos when people is posting about each neighborhood. We perform a language
separate treatment of the data and show that it can be extrapolated to a
tourists and locals separate analysis, and that tourism is reflected in Social
Media at a neighborhood level. The presented pipeline allows analyzing the
differences between the images that tourists and locals associate to the
different neighborhoods. The proposed method, which can be extended to other
cities or subjects, proves that Instagram data can be used to train multi-modal
(image and text) machine learning models that are useful to analyze
publications about a city at a neighborhood level. We publish the collected
dataset, InstaBarcelona and the code used in the analysis.
