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GeoICMF: Twitter User Geolocation Based on Implicit Location Correlation and Multi-Scale Feature Fusion

Shuaihui Zhu et al.

ACM Transactions on Information Systems2026https://doi.org/10.1145/3797894article
ABDC C
Weight
0.50

What the paper says

The geographic location of social media users is crucial for understanding user behavior, optimizing advertising, and supporting location-based services such as emergency awareness and event monitoring services. However, existing Twitter user geolocation methods primarily focus on explicit social relationships between users while overlooking implicit location correlations, which affects the accuracy of user geolocation. To address this, this paper proposes a Twitter user geolocation method (GeoICMF) based on implicit location correlations and multi-scale feature fusion. GeoICMF introduces a novel location association graph construction method to effectively capture implicit location correlations among users, an innovative multi-scale feature fusion model to dynamically fuse multi-scale features and generate richer user representations, and a pioneering geographic partitioning method to better adapt to user location distributions and enhance geolocation accuracy. Extensive experiments on three real-world datasets demonstrate that GeoICMF outperforms state-of-the-art baseline methods in Twitter user geolocation tasks, validating the effectiveness and superiority of the proposed method.

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https://doi.org/https://doi.org/10.1145/3797894

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@article{shuaihui2026,
  title        = {{GeoICMF: Twitter User Geolocation Based on Implicit Location Correlation and Multi-Scale Feature Fusion}},
  author       = {Shuaihui Zhu et al.},
  journal      = {ACM Transactions on Information Systems},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1145/3797894},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.