WITHDRAWN: Uncovering the Location of Twitter Users

• Activity Number: 33
• Type: Contributed
• Date/Time: Sunday, August 3, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Renato Assuncao
• Companies: UFMG
• Keywords: spatial analysis ; Markov random field ; relational model ; Bayesian analysis ; network model

Abstract

Social networks, like Twitter and Facebook, are valuable sources to monitor real-time events, such as earthquakes and epidemics. For this type of surveillance the user's location is an essential piece of information but a substantial number of users choose to not disclose their geographical information. However, characteristics of the users' behavior, such as the friends they associate with and the types of messages published can hint on their spatial location. In this work, we present a method to infer the spatial location of Twitter users. Unlike the approaches presented so far, we incorporate two sources of information to learn the geographical position, the text posted by users and their friendship network. We propose a probabilistic approach that jointly models the geographical labels and the Twitter texts of the users organized in the form of a graph representing the friendship network. We use 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 demonstrate the utility of this model in a la