Retweeting Prediction Based on Social Hotspots and Dynamic Tensor Decomposition

Qian LI  Xiaojuan LI  Bin WU  Yunpeng XIAO  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.5   pp.1380-1392
Publication Date: 2018/05/01
Publicized: 2018/01/30
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDP7364
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
social network,  hotspot topic,  behavior analysis,  retweeting prediction,  tensor decomposition,  

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In social networks, predicting user behavior under social hotspots can aid in understanding the development trend of a topic. In this paper, we propose a retweeting prediction method for social hotspots based on tensor decomposition, using user information, relationship and behavioral data. The method can be used to predict the behavior of users and analyze the evolvement of topics. Firstly, we propose a tensor-based mechanism for mining user interaction, and then we propose that the tensor be used to solve the problem of inaccuracy that arises when interactively calculating intensity for sparse user interaction data. At the same time, we can analyze the influence of the following relationship on the interaction between users based on characteristics of the tensor in data space conversion and projection. Secondly, time decay function is introduced for the tensor to quantify further the evolution of user behavior in current social hotspots. That function can be fit to the behavior of a user dynamically, and can also solve the problem of interaction between users with time decay. Finally, we invoke time slices and discretization of the topic life cycle and construct a user retweeting prediction model based on logistic regression. In this way, we can both explore the temporal characteristics of user behavior in social hotspots and also solve the problem of uneven interaction behavior between users. Experiments show that the proposed method can improve the accuracy of user behavior prediction effectively and aid in understanding the development trend of a topic.