Piotrkowicz, A orcid.org/0000-0002-7723-699X, Dimitrova, VG orcid.org/0000-0002-7001-0891, Otterbacher, J et al. (1 more author) (2017) Headlines Matter: Using Headlines to Predict the Popularity of News Articles on Twitter and Facebook. In: Proceedings of the Eleventh International AAAI Conference on Web and Social Media (ICWSM 2017). 11th International AAAI Conference on Web and Social Media (ICWSM-17), 15-18 May 2017, Montreal, Canada. Association for the Advancement of Artificial Intelligence , pp. 656-659. ISBN 978-1-57735-788-9
Abstract
Social media like Facebook or Twitter have become an entry point to news for many readers. In that scenario, the headline is the most prominent – and often the only visible – part of the news article. We propose a novel task of using only headlines to predict the popularity of news articles. The prediction model is evaluated on headlines from two major broadsheet news outlets – The Guardian and New York Times. We significantly improve over several baselines, noting differences in the model performance between Facebook and Twitter.
Metadata
Authors/Creators: |
|
---|---|
Keywords: | text analytics; prediction models; social media popularity; news headlines |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 Apr 2017 10:02 |
Last Modified: | 06 Jul 2018 10:19 |
Published Version: | https://www.aaai.org/Library/ICWSM/icwsm17contents... |
Status: | Published |
Publisher: | Association for the Advancement of Artificial Intelligence |
Identification Number: | https://doi.org/10.5518/174 |
Related URLs: |