Survey of User Geographic Location Prediction Based on Online Social Network

Liu Leyuan; Dai Yurou; Cao Yanan; Zhou Fan

doi:10.7544/issn1000-1239.202220417

Journal of Computer Research and Development > 2024 > 61(2): 385-412. > DOI: 10.7544/issn1000-1239.202220417 CSTR: 32373.14.issn1000-1239.202220417

Liu Leyuan, Dai Yurou, Cao Yanan, Zhou Fan. Survey of User Geographic Location Prediction Based on Online Social Network[J]. Journal of Computer Research and Development, 2024, 61(2): 385-412. DOI: 10.7544/issn1000-1239.202220417

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Survey of User Geographic Location Prediction Based on Online Social Network

1.
School of Information and Software Engineering, University of Electronic Science and Technology of China , Chengdu 610054
2.
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093

Funds: This work was supported by the National Natural Science Foundation of China (62176043, 62072077, 62272089), the Natural Science Foundation of Sichuan province (2022NSFSC0505), and the Sichuan Science and Technology Program (2022YFSY0006).

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Author Bio:
Liu Leyuan: born in 1982. PhD, assistant professor. His main research interests include machine learning, graph learning, and social network data mining

Dai Yurou: born in 1997. Master, research assistant. Her main research interests include data mining, machine learning, computer vision, and autonomous driving

Cao Yanan: born in 1985. PhD, professor. Member of CCF. Her main research interests include natural language processing, social network analysis, and machine learning

Zhou Fan: born in 1981. PhD, professor. His main research interests include machine learning, neural networks, spatial-temporal data analysis, graph learning, recommender systems, and social networks data mining
Received Date: May 20, 2022
Revised Date: April 02, 2023
Available Online: November 09, 2023

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Abstract

Abstract

With the increasing popularity of intelligent mobile terminals, people increasingly use social network platforms (such as Twitter, Sina Weibo, etc.) for information acquisition, comments, and exchanges. Although GPS devices can accurately obtain location information, many users do not directly share their location information for privacy and security considerations. Therefore, obtaining the geographic location of online users has become an important topic in both academia and industry and is the foundation of many downstream applications, such as location-based targeted advertising, event/location recommendations, early warning of natural disasters or diseases, and criminal tracking, etc. We survey in detail the methods, data types, evaluation metrics, and fundamental algorithms for predicting the geographic location of social network users. First, we discriminate different online user geolocation tasks and corresponding evaluation protocols. Subsequently, we assess the data structures and fusion methods used for individual geolocation tasks. Besides, we analyze the existing information extraction and feature selection approaches, as well as their advantages and disadvantages. Moreover, we provide a taxonomy to categorize existing user geolocation models and algorithms, followed by a thorough analysis of different methods from three aspects: geographic dictionary, traditional machine learning, deep learning and graph neural networks. Finally, we summarize the difficulties and challenges in user location prediction while outlining the possible research trend and opportunities to shed light on future work in this field.

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References (187)

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