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A Probabilistic Framework for Location Inference from Social Media

Yujie Qian,Jie Tang,Zhilin Yang,Binxuan Huang,Wei Wei,Kathleen M. Carley

Published 2017 in arXiv.org

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.

PUBLICATION RECORD

  • Publication year

    2017

  • Venue

    arXiv.org

  • Publication date

    2017-02-23

  • Fields of study

    Computer Science

  • Identifiers
    arXiv 1702.07281
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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