  Fashion is intertwined with external cultural factors, but identifying these
links remains a manual process limited to only the most salient phenomena. We
propose a data-driven approach to identify specific cultural factors affecting
the clothes people wear. Using large-scale datasets of news articles and
vintage photos spanning a century, we introduce a multi-modal statistical model
to detect influence relationships between happenings in the world and people's
choice of clothing. Furthermore, we apply our model to improve the concrete
vision tasks of visual style forecasting and photo timestamping on two
datasets. Our work is a first step towards a computational, scalable, and
easily refreshable approach to link culture to clothing.
