Year: 2015
Type: conference-paper
Abstract: Twitter is often used in quantitative studies that identify geographically-preferred topics, writing styles, and entities.These studies rely on either GPS coordinates attached to individual messages, or on the user-supplied location field in each profile.In this paper, we compare these data acquisition techniques and quantify the biases that they introduce; we also measure their effects on linguistic analysis and textbased geolocation.GPS-tagging and selfreported locations yield measurably different corpora, and these linguistic differences are partially attributable to differences in dataset composition by age and gender.Using a latent variable model to induce age and gender, we show how these demographic variables interact with geography to affect language use.We also show that the accuracy of text-based geolocation varies with population demographics, giving the best results for men above the age of 40.
DOI: 10.18653/v1/d15-1256
Authors: Umashanthi Pavalanathan, Jacob Eisenstein
Institution: Georgia Institute of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W1754225409