{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T01:41:27Z","timestamp":1772502087561,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T00:00:00Z","timestamp":1761868800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62272163"],"award-info":[{"award-number":["62272163"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Geolocating Twitter users from social media data holds significant value in applications such as targeted advertising, disaster response, and social network analysis. However, existing social network-based geolocation methods tend to focus primarily on mention relations while neglecting other critical interactions like retweet relationships. Moreover, effectively integrating diverse social features remains a key challenge, which limits the overall performance of geolocation models. To address these issues, this paper proposes a novel Twitter user geolocation method based on multi-graph feature fusion with a gating mechanism, termed MGFGCN, which fully leverages heterogeneous social network information. Specifically, MGFGCN first constructs separate mention and retweet graphs to capture multi-dimensional user relationships. It then incorporates the Information Gain Ratio (IGR) to select discriminative keywords and generates Term Frequency\u2013Inverse Document Frequency (TF-IDF) features, thereby enhancing the semantic representation of user nodes. Furthermore, to exploit complementary information across different graph structures, we propose a Structure-aware Gated Fusion Mechanism (SGFM) that dynamically captures differences and interactions between nodes from each graph, enabling the effective fusion of node representations into a unified representation for subsequent location inference. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art baselines in the Twitter user geolocation task across two public datasets.<\/jats:p>","DOI":"10.3390\/ijgi14110424","type":"journal-article","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T05:28:43Z","timestamp":1761888523000},"page":"424","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Twitter User Geolocation Based on Multi-Graph Feature Fusion with Gating Mechanism"],"prefix":"10.3390","volume":"14","author":[{"given":"Qiongya","family":"Wei","sequence":"first","affiliation":[{"name":"School of Information Engineering, Longzi Lake Campus, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaqiong","family":"Qiao","sequence":"additional","affiliation":[{"name":"College of Cryptology and Cyber Science, Nankai University, Tianjin 300350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuaihui","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Longzi Lake Campus, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aobo","family":"Jiao","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Longzi Lake Campus, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingqing","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Cryptology and Cyber Science, Nankai University, Tianjin 300350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gardasevic, S., Jaiswal, A., Lamba, M., Funakoshi, J., Chu, K.H., Shah, A., Sun, Y., Pokhrel, P., and Washington, P. 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