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Title: Utilizing External Knowledge to Enhance Location Prediction for Twitter/X Users in Low Resource Settings
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 article, 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 dataset used for city-level predictions. Second, we explore the option of increasing the number of training examples by utilizing external resources to generatesynthetic users. Finally, we combine these two strategies, exploiting the benefits of both. We empirically evaluate our proposed techniques on multiple Twitter/X datasets 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.  more » « less
Award ID(s):
1934925 1934494
PAR ID:
10534431
Author(s) / Creator(s):
;
Publisher / Repository:
ACM Trans. Spatial Algorithms Syst
Date Published:
Journal Name:
ACM Transactions on Spatial Algorithms and Systems
Volume:
10
Issue:
3
ISSN:
2374-0353
Page Range / eLocation ID:
1 to 25
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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