A Solution to Tweet-Based User Identification Across Online Social Networks

Li, Yongjun; Zhang, Zhen; Peng, You

doi:10.1007/978-3-319-69179-4_18

Cited by 4 publications

(1 citation statement)

References 25 publications

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“…In user feature-based approaches, the semantic features of users are mined based on data such as user attributes and UGCs to determine whether they represent the same real user by computing the user similarity [11,16,[23][24][25][26][27][28][29]. During the account registration process, the username is a required item, which enables the naming habits of users to be mined; thus, the user similarity is most widely computed based on the username.…”

Section: User Alignmentmentioning

confidence: 99%

A Semantic-Enhancement-Based Social Network User-Alignment Algorithm

Huang

Zhao

Zhang

et al. 2023

Entropy

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User alignment can associate multiple social network accounts of the same user. It has important research implications. However, the same user has various behaviors and friends across different social networks. This will affect the accuracy of user alignment. In this paper, we aim to improve the accuracy of user alignment by reducing the semantic gap between the same user in different social networks. Therefore, we propose a semantically enhanced social network user alignment algorithm (SENUA). The algorithm performs user alignment based on user attributes, user-generated contents (UGCs), and user check-ins. The interference of local semantic noise can be reduced by mining the user’s semantic features for these three factors. In addition, we improve the algorithm’s adaptability to noise by multi-view graph-data augmentation. Too much similarity of non-aligned users can have a large negative impact on the user-alignment effect. Therefore, we optimize the embedding vectors based on multi-headed graph attention networks and multi-view contrastive learning. This can enhance the similar semantic features of the aligned users. Experimental results show that SENUA has an average improvement of 6.27% over the baseline method at hit-precision30. This shows that semantic enhancement can effectively improve user alignment.

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Section: User Alignmentmentioning

confidence: 99%

A Semantic-Enhancement-Based Social Network User-Alignment Algorithm

Huang

Zhao

Zhang

et al. 2023

Entropy

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Identifying Users Based on Time-Frequency Characteristics

Gao

Chen

et al. 2019

J. Phys.: Conf. Ser.

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With the rapid development of information technology, whether it is with regard to the personalized recommendation of the business or network warning by the public security organ, the identification of online users is a very important research topic. The user’s web browsing behavior data contains potential behavioral characteristics, in this paper, we utilize the time-frequency analysis method, which extracts the behavior characteristics when users accessing the network, so as to obtain the user behavior pattern and calculate the similarity between the behavior patterns for achieving the purpose of user identification. This method is verified by the weblog, and the experiment results show that the method extracts user network behavior features based on time-frequency analysis to ensure the accuracy of user identification.

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A Novel Two-Stage Framework for User Identification Across Social Networks

Yuan

et al. 2019

2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD)

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A Solution to Tweet-Based User Identification Across Online Social Networks

Cited by 4 publications

References 25 publications

A Semantic-Enhancement-Based Social Network User-Alignment Algorithm

A Semantic-Enhancement-Based Social Network User-Alignment Algorithm

Identifying Users Based on Time-Frequency Characteristics

A Novel Two-Stage Framework for User Identification Across Social Networks

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