We study the extent to which we can infer users' geographical locations from social media. Location inference from social media can bene t 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 Twi er 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 o ers several unique advantages for this task. First, the model generalizes previous methods by incorporating content, network, and deep features learned from social context. e model is also exible 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. ird, we validate the proposed model on three di erent genres of data -Twi er, 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.
REF proposed a probabilistic model, integrating the content and network features learned from social media, to predict the location of a user.
A Probabilistic Framework for Location Inference from Social Media
ArXiv
ArXiv
Computer Science