Content-based Dwell Time Engagement Prediction Model for News Articles

Heidar Davoudi, Aijun An, Gordon Edall


Abstract
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 predict the dwell time of an article before its release. This allows digital 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 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. The experimental results on a real dataset from a major newspaper show that the proposed model outperforms other state-of-the-art baselines.
Anthology ID:
N19-2028
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Anastassia Loukina, Michelle Morales, Rohit Kumar
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
226–233
Language:
URL:
https://aclanthology.org/N19-2028
DOI:
10.18653/v1/N19-2028
Bibkey:
Cite (ACL):
Heidar Davoudi, Aijun An, and Gordon Edall. 2019. Content-based Dwell Time Engagement Prediction Model for News Articles. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers), pages 226–233, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Content-based Dwell Time Engagement Prediction Model for News Articles (Davoudi et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-2028.pdf