Confounds and Consequences in Geotagged Twitter Data
This work addresses biases in geotagged Twitter data for researchers in computational social science, though it is incremental as it builds on existing methods to quantify known issues.
The study compared GPS-tagged and self-reported location data from Twitter, quantifying biases and their effects on linguistic analysis and text-based geolocation, finding that these methods yield different corpora influenced by age and gender demographics, with geolocation accuracy best for men over 40.
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 text-based geolocation. GPS-tagging and self-reported 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.