FewUser: Few-Shot Social User Geolocation via Contrastive Learning
Menglin Li Kwan Hui Lim
To address the challenges of scarcity in geotagged data for social user
geolocation, we propose FewUser, a novel framework for Few-shot social User
geolocation. We incorporate a contrastive learning strategy between users and
locations to improve geolocation performance with no or limited training data.
FewUser features a user representation module that harnesses a pre-trained
language model (PLM) and a user encoder to process and fuse diverse social
media inputs effectively. To bridge the gap between PLM's knowledge and
geographical data, we introduce a geographical prompting module with hard,
soft, and semi-soft prompts, to enhance the encoding of location information.
Contrastive learning is implemented through a contrastive loss and a matching
loss, complemented by a hard negative mining strategy to refine the learning
process. We construct two datasets TwiU and FliU, containing richer metadata
than existing benchmarks, to evaluate FewUser and the extensive experiments
demonstrate that FewUser significantly outperforms state-of-the-art methods in
both zero-shot and various few-shot settings, achieving absolute improvements
of 26.95\% and \textbf{41.62\%} on TwiU and FliU, respectively, with only one
training sample per class. We further conduct a comprehensive analysis to
investigate the impact of user representation on geolocation performance and
the effectiveness of FewUser's components, offering valuable insights for
future research in this area.