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
- 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)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/N19-2028.pdf