Text-Based Twitter User Geolocation Prediction

TLDR

Geographical location is essential for geospatial applications, yet prior work assumes that textual geographic references indicate author location, which is difficult to detect in informal, multilingual Twitter data. The study aims to improve text‑based Twitter user geolocation by developing an integrated framework and examining factors affecting accuracy. The authors evaluate various feature‑selection methods for location‑indicative words and assess the influence of non‑geotagged tweets, language, user metadata, temporal variance, and user geolocatability on prediction models. The framework achieves state‑of‑the‑art accuracy and offers comprehensive insights for designing robust text‑based geolocation systems.

Abstract

Geographical location is vital to geospatial applications like local search and event detection. In this paper, we investigate and improve on the task of text-based geolocation prediction of Twitter users. Previous studies on this topic have typically assumed that geographical references (e.g., gazetteer terms, dialectal words) in a text are indicative of its author’s location. However, these references are often buried in informal, ungrammatical, and multilingual data, and are therefore non-trivial to identify and exploit. We present an integrated geolocation prediction framework and investigate what factors impact on prediction accuracy. First, we evaluate a range of feature selection methods to obtain “location indicative words”. We then evaluate the impact of non-geotagged tweets, language, and user-declared metadata on geolocation prediction. In addition, we evaluate the impact of temporal variance on model generalisation, and discuss how users differ in terms of their geolocatability. We achieve state-of-the-art results for the text-based Twitter user geolocation task, and also provide the most extensive exploration of the task to date. Our findings provide valuable insights into the design of robust, practical text-based geolocation prediction systems.