Multiview Deep Learning for Predicting Twitter Users' Location

TL;DR

This paper introduces MENET, a multi-view deep learning model that combines content and network features to improve Twitter user geolocation prediction, outperforming existing methods on benchmark datasets.

Contribution

The paper presents a novel multi-entry neural network architecture that integrates textual, network, and metadata features for geolocation prediction.

Findings

MENET outperforms state-of-the-art methods on three benchmarks.

Combining multiple data representations improves geolocation accuracy.

Multi-scale cell subdivision enhances model generalization.

Abstract

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.