Exploiting Text and Network Context for Geolocation of Social Media Users

TL;DR

This paper combines text and network data to improve the automatic geolocation of social media users, demonstrating that hybrid methods outperform individual approaches across multiple datasets.

Contribution

It introduces a novel hybrid approach that integrates text-based and network-based geolocation methods, achieving state-of-the-art results.

Findings

Hybrid methods outperform individual approaches.

Text and network methods have comparable effectiveness.

Hybrid approach is especially beneficial with poorly connected user graphs.

Abstract

Research on automatically geolocating social media users has conventionally been based on the text content of posts from a given user or the social network of the user, with very little crossover between the two, and no bench-marking of the two approaches over compara- ble datasets. We bring the two threads of research together in first proposing a text-based method based on adaptive grids, followed by a hybrid network- and text-based method. Evaluating over three Twitter datasets, we show that the empirical difference between text- and network-based methods is not great, and that hybridisation of the two is superior to the component methods, especially in contexts where the user graph is not well connected. We achieve state-of-the-art results on all three datasets.