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The problem of predicting the location of users on large social networks likeTwitter has emerged from real-life applications such as social unrest detectionand online marketing. Twitter user geolocation is a difficult and activeresearch topic with a vast literature. Most of the proposed methods followeither a content-based or a network-based approach. The former exploitsuser-generated content while the latter utilizes the connection or interactionbetween Twitter users. In this paper, we introduce a novel method combining thestrength of both approaches. Concretely, we propose a multi-entry neuralnetwork architecture named MENET leveraging the advances in deep learning andmultiview learning. The generalizability of MENET enables the integration ofmultiple data representations. In the context of Twitter user geolocation, werealize MENET with textual, network, and metadata features. Considering thenatural distribution of Twitter users across the concerned geographical area,we subdivide the surface of the earth into multi-scale cells and train MENETwith the labels of the cells. We show that our method outperforms the state ofthe art by a large margin on three benchmark datasets.

Multiview Deep Learning for Predicting Twitter Users' Location