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.

通过 Instagram 数据集的图像和字幕对建立多模态机器学习模型的训练，对巴塞罗那的不同社区进行了区域性的分析，以期解决巨大的旅游业对城市产生的影响问题。

从巴塞罗那 Instagram 数据中学习本地居民和游客关于该市各个社区的发帖信息

Learning from #Barcelona Instagram data what Locals and Tourists post  about its Neighbourhoods

This is the Proceedings of NIPS 2016 Workshop on Interpretable Machine
Learning for Complex Systems, held in Barcelona, Spain on December 9, 2016

2016 年 NIPS 工作坊于西班牙巴塞罗那举行，主题为可解释的机器学习和复杂系统研究。