  We study the extent to which we can infer users' geographical locations from
social media. Location inference from social media can benefit many
applications, such as disaster management, targeted advertising, and news
content tailoring. The challenges, however, lie in the limited amount of
labeled data and the large scale of social networks. In this paper, we
formalize the problem of inferring location from social media into a
semi-supervised factor graph model (SSFGM). The model provides a probabilistic
framework in which various sources of information (e.g., content and social
network) can be combined together. We design a two-layer neural network to
learn feature representations, and incorporate the learned latent features into
SSFGM. To deal with the large-scale problem, we propose a Two-Chain Sampling
(TCS) algorithm to learn SSFGM. The algorithm achieves a good trade-off between
accuracy and efficiency. Experiments on Twitter and Weibo show that the
proposed TCS algorithm for SSFGM can substantially improve the inference
accuracy over several state-of-the-art methods. More importantly, TCS achieves
over 100x speedup comparing with traditional propagation-based methods (e.g.,
loopy belief propagation).
