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• W4286377514 abstract "Recently, with the rise of social media, the research of information diffusion prediction has drawn much attention from scholars. The problem has important applications in public opinion monitoring, social advertising, etc. Through an in-depth diffusion analysis, we observed an interesting phenomenon where a piece of message may endure a “second rise” after it peaked at the maximum popularity for a while. However, this phenomenon has not yet been investigated by existing works. Moreover, the valuable information contained in the repost comments were not fully utilized. To fill this gap, this paper proposes a graph neural network-based model for predicting second rise of information diffusion. Specifically, we first design a simple but efficient algorithm to determine whether a message has second rise. Then, text analysis is carried out on the repost comments, and different message-text bipartite graphs are constructed according to different types of textual information (topics, comments, @users, emojis). After that, we use random walk to generate node sequences and apply skip-gram model to learn node representations, followed by a PCA-based dimension deduction. The compressed textual features are combined with embeddings learned from repost network, before they are finally fed to downstream machine learning models to generate predictions. Experimental results on the Weibo dataset show that the overall prediction accuracy can be improved significantly by incorporating the textual features." @default.
• W4286377514 created "2022-07-21" @default.
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• W4286377514 date "2022-01-01" @default.
• W4286377514 modified "2023-09-26" @default.
• W4286377514 title "A Graph Neural Network-Based Approach for Predicting Second Rise of Information Diffusion on Social Networks" @default.
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• W4286377514 doi "https://doi.org/10.1007/978-981-19-4549-6_27" @default.
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