Exploring multiple evidence to inferusers’ location in twitter.
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Date
2015
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Abstract
Online social networks are valuable sources of information to monitor real-time events,
such as
earthquakes and epidemics. For this type of surveillance, users’ location is an
essential piece of information, but a substantial number of users choose not to disclose their
geographical location. However, characteristics of the users' behavior, such as the friends they
associate with and the types of messages published may hint on their spatial location. In this
paper, we propose a method to infer the spatial location of Twitter users. Unlike the approaches
proposed so far, it incorporates two sources of information to learn geographical position: the
text posted by users and their friendship network. We propose a probabilistic approach that
jointly models the geographical labels and Twitter texts of users organized in the form of a graph
representing the friendship network. We use the Markov random field probability model to represent
the network, and learning is carried out through a Markov Chain Monte Carlo simulation technique
to approximate the posterior probability distribution of the missing geographical labels. We show
the accuracy of the algorithm in a large dataset of Twitter users, where the ground truth is the
location given by GPS. The method presents promising results, with little
sensitivity to parameters and high values of precision.
Description
Keywords
Networklearning, Location inference, Twitter userlocation
Citation
RODRIGUES, E. C. et al. Exploring multiple evidence to inferusers’ location in twitter. Neurocomputing, Amsterdam, v. 171, p. 30-38, 2015. Disponível em: <http://www.sciencedirect.com/science/article/pii/S092523121500764X>. Acesso em: 16 jan. 2018.