The problem of predicting the location of users on large social networks like
Twitter has emerged from real-life applications such as social unrest detection
and online marketing. Twitter user geolocation is a difficult and active
research topic with a vast literature. Most of the proposed methods follow
either a content-based or a network-based approach. The former exploits
user-generated content while the latter utilizes the connection or interaction
between Twitter users. In this paper, we introduce a novel method combining the
strength of both approaches. Concretely, we propose a multi-entry neural
network architecture named MENET leveraging the advances in deep learning and
multiview learning. The generalizability of MENET enables the integration of
multiple data representations. In the context of Twitter user geolocation, we
realize MENET with textual, network, and metadata features. Considering the
natural distribution of Twitter users across the concerned geographical area,
we subdivide the surface of the earth into multi-scale cells and train MENET
with the labels of the cells. We show that our method outperforms the state of
the art by a large margin on three benchmark datasets.

本文提出了一种名为 MENET (Multi-Entry Neural Network Architecture) 的深度学习模型，能够综合使用利用 Twitter 用户生成内容、用户间连接性以及元数据等多种数据表示方式进行 Twitter 用户地理位置预测，且在三个基准数据集上表现显著优于现有方法。