Repost prediction incorporating time-sensitive mutual influence in social networks

摘要

Nowadays, information can spread rapidly over social networks via the relationships and interactions among people. To reveal the underlying intricate mechanism of information propagation, the problem of repost behavior prediction has recently drawn extensive attention. In this paper, we propose a novel method to measure time-sensitive mutual influence based on temporal behavior patterns of users and develop an efficient algorithm to calculate it via a discretization method. To predict repost behavior more accurately, we introduce another two features of user interests and information content, and respectively design the effective measurements of them to capture their predictive power. We further combine time-sensitive mutual influence with the two features into a feature-based method and evaluate the performance of the proposed method on repost prediction. Finally, extensive experiments have been conducted on a real large-scale microblogging dataset. The experimental results demonstrate that our method can achieve better performance compared to several baseline methods. (C) 2018 Elsevier B.V. All rights reserved.