  Tumblr, as a leading content provider and social media, attracts 371 million
monthly visits, 280 million blogs and 53.3 million daily posts. The popularity
of Tumblr provides great opportunities for advertisers to promote their
products through sponsored posts. However, it is a challenging task to target
specific demographic groups for ads, since Tumblr does not require user
information like gender and ages during their registration. Hence, to promote
ad targeting, it is essential to predict user's demography using rich content
such as posts, images and social connections. In this paper, we propose graph
based and deep learning models for age and gender predictions, which take into
account user activities and content features. For graph based models, we come
up with two approaches, network embedding and label propagation, to generate
connection features as well as directly infer user's demography. For deep
learning models, we leverage convolutional neural network (CNN) and multilayer
perceptron (MLP) to prediction users' age and gender. Experimental results on
real Tumblr daily dataset, with hundreds of millions of active users and
billions of following relations, demonstrate that our approaches significantly
outperform the baseline model, by improving the accuracy relatively by 81% for
age, and the AUC and accuracy by 5\% for gender.
