Title: A Probabilistic Framework for Location Inference from Social Media,
Abstract: 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. In recent years, a number of algorithms have been proposed
for identifying user locations on social media platforms such as Twitter and
Facebook from message contents, friend networks, and interactions between
users. In this paper, we propose a novel probabilistic model based on factor
graphs for location inference that offers several unique advantages for this
task. First, the model generalizes previous methods by incorporating content,
network, and deep features learned from social context. The model is also
flexible enough to support both supervised learning and semi-supervised
learning. Second, we explore several learning algorithms for the proposed
model, and present a Two-chain Metropolis-Hastings (MH+) algorithm, which
improves the inference accuracy. Third, we validate the proposed model on three
different genres of data - Twitter, Weibo, and Facebook - and demonstrate that
the proposed model can substantially improve the inference accuracy (+3.3-18.5%
by F1-score) over that of several state-of-the-art methods.