Utilizing External Knowledge to Enhance Location Prediction for Twitter/X Users in Low Resource Settings

Abstract

Accurate estimates of user location are important for many online services, including event detection, disaster management and determining public opinion. Neural network-based techniques have proven to be highly effective in predicting user location. However, these models typically require a large amount of labeled training data, which can be difficult to obtain in real-world scenarios. In this paper, we present two approaches to tackle the issue of limited training data when predicting city level location. First, we consider a self-supervised approach that trains a state level model without labeled data and then integrate this knowledge into the training data set used for city level predictions. Second, we explore the option of increasing the number of training examples by utilizing external resources to generate synthetic users . Finally, we combine these two strategies, exploiting the benefits of both. We empirically evaluate our proposed techniques on multiple Twitter/X data sets and show that our models perform significantly better than the state-of-the-art with improvements of up to 6% for Acc@161 and 8% for F1 score.