Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/handle/123456789/9394
Title: Exploring multiple evidence to inferusers’ location in twitter.
Authors: Rodrigues, Erica Castilho
Assunção, Renato Martins
Pappa, Gisele Lobo
Renno, Diogo
Keywords: Networklearning
Location inference
Twitter userlocation
Issue Date: 2015
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.
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.
URI: http://www.repositorio.ufop.br/handle/123456789/9394
metadata.dc.identifier.uri2: http://www.sciencedirect.com/science/article/pii/S092523121500764X
ISSN: 09252312
Appears in Collections:DEEST - Artigos publicados em periódicos

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