A Novel Neural Approach for News Reprint Prediction

News media has become a prevalent information spreading platform, where news sites can reprint news from other sites. To better understand the mechanism of news propagation, it is necessary to model reprint behavior and predict whether a news site will reprint a piece of news. Most existing works in news reprint analysis focus on analyzing the semantic of news content, little work has been done on integrating reprint relationship among sites and news content for reprint prediction from the perspective of sites. The challenge of improving prediction performance lies in how to effectively incorporate these two kinds of information to learn a more comprehensive reprint behavior model. In this paper, we propose an Integrated Neural Reprint Prediction (INRP) model that considers both reprint relationship and news content. It models the reprint relationships as a directed weighted graph and maps it into a latent space to learn sites representations. During news content modeling process, sites representations are embedded as attention guidance to build up more site-specific content representations. Finally, sites and news representations are jointly modeled to predict whether a piece of news will be reprinted by a site. We empirically evaluate the performance of the proposed model on a real world dataset. Experimental results show that taking both the reprint relationship and news content information into consideration could allow us make more accurate analysis of reprint patterns. The mined patterns could serve as a feedback channel for both corporations and management departments.

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