Multiview Deep Learning for Predicting Twitter Users' Location
This addresses the need for accurate user geolocation in applications like social unrest detection and online marketing, representing an incremental improvement over existing methods.
The paper tackles the problem of predicting Twitter users' locations by combining content-based and network-based approaches using a multi-entry neural network (MENET) with textual, network, and metadata features, and it outperforms state-of-the-art methods by a large margin on three benchmark datasets.
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.