The article dwell time (i.e., expected time that users spend on an article) is among the most important factors showing the article engagement. It is of great interest to news agencies to predict the dwell time of an article before its release. It allows online newspapers to make informed decisions and publish more engaging articles. In this paper, we propose a novel content-based approach based on a deep neural network architecture for predicting article dwell times. The proposed model extracts emotion, event and entity-based features from an article, learns interactions among them, and combines the interactions with the word-based features of the article to learn a model for predicting the dwell time. We apply the proposed model to a real dataset from a national newspaper showing that the proposed model outperforms other state-of-the-art baselines.
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