Abstract
User identification across social networks uses various user information to determine whether two accounts from different social networks belong to the same user. The most intuitive method is to use user profiles to solve user identification across social networks. How to effectively learn the characteristics of user profiles is crucial. This paper proposes a model for user identification across social networks based on multi-modal information fusion of user profiles. First, the pre-trained model is used in deep learning to obtain the text feature vector and image feature vector of the user profile. This paper fused the multi-modal feature vectors in the user profile. Then, the feature vector sequence is fed into the bidirectional LSTM model with an attention mechanism. Finally, the probability result of user identification is obtained through Multilayer Perceptron. Experimental results show that the suggested technique outperforms state-of-the-art baselines when evaluated on three real-world datasets. Our approach outperforms solutions based on image pixel-level comparison regarding user identification challenges, thanks to the semantic feature mining of photos in user profiles.
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Ye, C., Yang, J., Mao, Y. (2024). Fusion of Multi-modal Information of User Profile Across Social Networks for User Identification. In: Huang, DS., Zhang, X., Guo, J. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14866. Springer, Singapore. https://doi.org/10.1007/978-981-97-5594-3_35
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DOI: https://doi.org/10.1007/978-981-97-5594-3_35
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