Learning to Determine the Quality of News Headlines

Amin Omidvar, Hossein Pourmodheji, Aijun An, Gordon Edall

2020

Abstract

Today, most news readers read the online version of news articles rather than traditional paper-based newspapers. Also, news media publishers rely heavily on the income generated from subscriptions and website visits made by news readers. Thus, online user engagement is a very important issue for online newspapers. Much effort has been spent on writing interesting headlines to catch the attention of online users. On the other hand, headlines should not be misleading (e.g., clickbaits); otherwise readers would be disappointed when reading the content. In this paper, we propose four indicators to determine the quality of published news headlines based on their click count and dwell time, which are obtained by website log analysis. Then, we use soft target distribution of the calculated quality indicators to train our proposed deep learning model which can predict the quality of unpublished news headlines. The proposed model not only processes the latent features of both headline and body of the article to predict its headline quality but also considers the semantic relation between headline and body as well. To evaluate our model, we use a real dataset from a major Canadian newspaper. Results show our proposed model outperforms other state-of-the-art NLP models.

Paper Citation

in Harvard Style

Omidvar A., Pourmodheji H., An A. and Edall G. (2020). Learning to Determine the Quality of News Headlines. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: NLPinAI, ISBN 978-989-758-395-7, pages 401-409. DOI: 10.5220/0009367504010409

in Bibtex Style

@conference{nlpinai20,
author={Amin Omidvar and Hossein Pourmodheji and Aijun An and Gordon Edall},
title={Learning to Determine the Quality of News Headlines},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: NLPinAI,},
year={2020},
pages={401-409},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009367504010409},
isbn={978-989-758-395-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: NLPinAI,
TI - Learning to Determine the Quality of News Headlines
SN - 978-989-758-395-7
AU - Omidvar A.
AU - Pourmodheji H.
AU - An A.
AU - Edall G.
PY - 2020
SP - 401
EP - 409
DO - 10.5220/0009367504010409