User Identification with Spatio-Temporal Awareness across Social Networks

Gao, Xing; Ji, Wei; Li, Yongjun; Deng, Yao; Dong, Wei

doi:10.1145/3269206.3269248

Cited by 12 publications

(3 citation statements)

References 7 publications

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“…In recent advances [35] [4] [36], researchers focused on using location data to achieve user account linkage. By utilizing the user-generated location data in social media platforms, a co-clustering-based framework was proposed [35], where account clusterings in spatial and temporal and dimensions were carried out synchronously.…”

Section: Cross-platform User Linkagementioning

confidence: 99%

HFUL: A Hybrid Framework for User Account Linkage across Location-Aware Social Networks

Chen¹,

Wang²,

Yin³

et al. 2022

Preprint

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Sources of complementary information are connected when we link user accounts belonging to the same user across different platforms or devices. The expanded information promotes the development of a wide range of applications, such as cross-platform prediction, cross-platform recommendation, and advertisement. Due to the significance of user account linkage and the widespread popularization of GPS-enabled mobile devices, there are increasing research studies on linking user account with spatio-temporal data across location-aware social networks. Being different from most existing studies in this domain that only focus on the effectiveness, we propose a novel framework entitled HFUL (A Hybrid Framework for User Account Linkage across Location-Aware Social Networks), where efficiency, effectiveness, scalability, robustness, and application of user account linkage are considered. Specifically, to improve the efficiency, we develop a comprehensive index structure from the spatio-temporal perspective, and design novel pruning strategies to reduce the search

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Section: Cross-platform User Linkagementioning

confidence: 99%

HFUL: A Hybrid Framework for User Account Linkage across Location-Aware Social Networks

Chen¹,

Wang²,

Yin³

et al. 2022

Preprint

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show abstract

“…It can prove that these attributes are helpful for user identification. Some existing works only use one attribute for user identification, such as only use display name [11,13,[26][27][28], only use profile photos [29], and only use locations [30][31][32][33]. ese studies prove the feasibility of one attribute to perform user identification.…”

Section: Related Workmentioning

confidence: 99%

User Identification Based on Integrating Multiple User Information across Online Social Networks

Zeng

Tang

Wang

et al. 2021

Security and Communication Networks

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User identification can help us build more comprehensive user information. It has been attracting much attention from academia. Most of the existing works are profile-based user identification and relationship-based user identification. Due to user privacy settings and social network restrictions on user data crawl, user data may be missing or incomplete in real social networks. User data include profiles, user-generated contents (UGCs), and relationships. The features extracted in previous research may be sparse. In order to reduce the impact of the above problems on user identification, we propose a multiple user information user identification framework (MUIUI). Firstly, we develop multiprocess crawlers to obtain the user data from two popular social networks, Twitter and Facebook. Secondly, we use named entity recognition and entity linking to obtain and integrate locations and organizations from profiles and UGCs. We also extract URLs from profiles and UGCs. We apply the locations jointly with the relationships and develop several algorithms to measure the similarity of the display name, all locations, all organizations, location in profile, all URLs, following organizations, and user ID, respectively. Afterward, we propose a fusion classifier machine learning-based user identification method. The results show that the F1 score of MUIUI reaches 86.46% on the dataset. It proves that MUIUI can reduce the impact of user data that are missing or incomplete.

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“…The value of UGSTs has been attracting considerable attention. Furthermore, UGSTs with fine-grained geolocation [2] can benefit many location-based applications, such as smart health [3], emergency analysis [4], [5], event detection [6], and user identification [7], [8], [9].…”

Section: Introductionmentioning

confidence: 99%