Twitter User Geolocation Using a Unified Text and Network Prediction Model
Not on the map. Why?
Abstract (arXiv)
We propose a label propagation approach to geolocation prediction based on Modified Adsorption, with two enhancements:(1) the removal of "celebrity" nodes to increase location homophily and boost tractability, and (2) he incorporation of text-based geolocation priors for test users. Experiments over three Twitter benchmark datasets achieve state-of-the-art results, and demonstrate the effectiveness of the enhancements.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Jun 2015.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1; 2025: 0; 2026: 1.
4 papers.
- 2022Geographic Adaptation of Pretrained Language Modelscited by 14
- 2024Towards Interactive Global Geolocation Assistantcited by 11
- 2021Designing weighted and multiplex networks for deep learning user geolocation in Twittercited by 1
- 2026ValueGraph: Value-Signal Guided Graph Pre-training for Contextualized User Representationcited by 0
- 2026ValueGraph: Value-Signal Guided Graph Pre-training for Contextualized User Representation30 Aug 2026
- 2024Towards Interactive Global Geolocation Assistant12 Dec 2024
- 2022Geographic Adaptation of Pretrained Language Models16 Mar 2022
- 2021Designing weighted and multiplex networks for deep learning user geolocation in Twitter13 Dec 2021